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Past seminars

(Upcoming seminars are visible in the calendar.)

2018

December 5: Zhaohui Luo "Dependent Event Types"

Slides

This talk studies how dependent types can be employed for a refined treatment of event types, offering a nice improvement to Davidson's event semantics. We consider dependent event types indexed by thematic roles (DETs) and illustrate how, in the presence of refined event types, subtyping plays an essential role in semantic interpretations.

Two applications of DETs are studied. The first shows that DETs give a natural solution to an incompatibility problem (sometimes called event quantification problem) in combining event semantics with the traditional compositional semantics. The second concerns selectional restriction: it is shown that DETs offer flexible but nice treatments of selectional restriction in the MTT-semantic setting with events.


December 3: Zhaohui Luo "Universes in MTT-semantics"

Slides

In type theory, a universe is a type of types. Universes play important roles when modern type theories (MTTs) are employed as foundational languages for linguistic semantics. In this talk, I'll report work on two kinds of universes in the study of MTT-semantics. The first kind may be called linguistic universes which include CN, the universe of common nouns, and LType, the universe employed in the study of coordination. It is shown how they are introduced and used in semantic studies and, in particular, their usefulness is reflected in how they facilitate \Pi-polymorphism in various semantic formalisations.

I shall then study logical universes. In order to formulate MTT-semantics adequately, proof irrelevance needs to be enforced in the underlying type theory. For example, in type theory UTT, this is possible because there is the universe Prop of all logical propositions. However, in Martin-Löf's type theory, this is impossible because types and propositions are identified in MLTT. I propose that the extension of MLTT with h-logic, as developed in the HoTT project, can be used adequately as a foundational language for MTT-semantics, since there is a built-in notion of proof irrelevance in h-logic.


November 28: Devdatt Dubhashi, Mikael Kågebäck and Asad Sayeed "Learning (a language) to Communicate Efficiently"

Slides

Although languages vary enormously, there are nevertheless universal tendencies in word meanings, such that similar or identical meanings often appear in unrelated languages. A major question is how to account for such semantic universals and variation of the lexicon in a principled and unified way. An influential approach to this question proposes that word meanings may reflect adaptation to pressure for efficient communication -- this principle holds that languages are under pressure to be simultaneously informative (so as to support effective communication) and simple (so as to minimize cognitive load). We offer computational support for this principle in the domain of color words i.e, how languages partition the semantic space of colours by linguistic terms. Our framework uses reinforcement learning for automated agents to generate partitions that are efficient and consistent with those found in many languages in the World Colour Survey. We argue that our framework provides a flexible and powerful tool to address similar fundamental questions about universals in other domains as well.


November 21: Bartosz Wieckowski "Intuitionistic multi-agent subatomic natural deduction for belief and knowledge"

Slides

In this talk, we will consider a natural deduction system which aims at the proof-theoretic analysis of reasoning with complex multi-agent belief (resp. knowledge) constructions (involving, e.g., forms of reciprocating or universal belief, or intentional identity). Making use of a normalization result for the system, we shall propose a proof-theoretic semantics for the intensional operators for intuitionistic belief and knowledge which explains their meaning entirely by appeal to the structure of derivations. Since the system enjoys the subexpression property, a refinement of the subformula property, it is fully analytic. We will also compare this approach to the logic and semantics of belief and knowledge with other intuitionistic approaches.


November 20: Ielka van der Sluis "The PAT project: Annotation and Evaluation of Pictures and Text"

SlidesSlides

In this talk I will present the PAT project in which we investigate the use, effects and optimisation of documents that contain pictures and text (PAT). While the benefit of including pictures has been established, the design of pictures, text, and picture-text relation(s) has not been researched in a systematic manner. PAT aims to gain an in-depth understanding of their characteristics to augment existing theories on cognitive processing of multimodal presentations. Resulting models will be validated by implementing them in natural language generation algorithms and comparing their output to human-authored presentations.

The PAT project launches a methodical investigation of multimodal instructions (MIs) used in first-aid practices to help people in need. Currently, there are no guidelines for the design of MIs that effectively instruct people to operate an AED, place a victim in a recovery position, remove ticks etc. The huge variations in pictorial and verbal means employed in these instructions demonstrate the urgency to obtain validated guidelines based on empirical evidence collected from readers and users. Investigating multimodality in these MIs allows us to evaluate the effectiveness of combining pictures and text in a practical context focussing on e.g. attention, comprehension, recall, user judgements, and task performance.

The PAT project makes use of a annotated corpus of MIs and a workbench that has been developed for the annotation and retrieval of the MIs. The MIs are first-aid instructions that appear in Het Oranje Kruisboekje and variations of these instructions from other sources, like hospitals, health and safety organisations and the internet.

In the PAT project approaches from Information Design Research and Computational Linguistics employing corpus collection and analysis, (automatic) annotation, experimentation, and natural language generation are combined. The project will deliver theoretical results in terms of empirically validated models for effective MIs. Results of practical value include new annotated multimodal corpora, implemented taggers to automatically annotate potentially effective properties of MIs, algorithms to automatically generate effective text-picture combinations and authoring guidelines to produce good quality instructions.

Project website: https://www.rug.nl/let/pat

Lecturer: Ielka van der Sluis detailed CV: https://www.rug.nl/staff/i.f.van.der.sluis/


November 14: Patrick Blackburn "The clarification potential of instructions: Predicting clarification requests"

Slides

The hypothesis motivating this talk is that conversational implicatures are an important source of clarification requests, and in this talk I will do two main things. First, I will motivate the hypothesis in theoretical, practical and empirical terms and formulate it as a concrete Clarification Potential Principle: implicatures may become explicit as fourth-level clarification requests. Second, I will present a framework for generating the clarification potential of an instruction by inferring its conversational implicatures with respect to a particular context. I will discuss the evaluation of the framework, illustrate its performance using a human-human corpus of situated conversations, and argue that much of the inference required can be handled using classical planning.

This talk is based on joint work with Luciana Benotti of Logic, Interaction and Intelligent Systems Group, Universidad Nacional de Córdoba, Argentina.

Many of the main ideas can be found in the paper: Modeling the clarification potential of instructions: Predicting clarification requests and other reactions, by Luciana Benotti and Patrick Blackburn, Computer Speech & Language 45: 536-551 (2017)


October 31: Bill Noble "Measuring linguistic style alignment: Social and psychological perspectives"

Slides

In conversation, speakers tend to adapt their speech to be more similar to that of their interlocutor. Such alignment is observed across various linguistic phenomena. In this talk, we will consider linguistic style alignment and some ways to measure it. We will also explore whether lingistic style alignment is sensitive to social factors, such as social network centrality, or if it can be explained by automatic psychological priming alone.


October 24: Marco Baroni "Systematic compositionality in recurrent neural networks (and, if time allows, humans) (joint work with Brenden Lake, João Loula, Adam Liska, Germán Kruszewski, Tal Linzen)"

Slides

Recurrent neural networks (RNNs) are remarkably general learning systems that, given appropriate training examples, can handle complex sequential processing tasks, such as those frequently encountered in language and reasoning. However, RNNs are remarkably sample-heavy, typically requiring hundreds of thousands of examples to master tasks that humans can solve after just a few exposures. The first set of experiments I will present shows that modern RNNs, just like their nineties ancestors, have problems with systematic compositionality, that is, the ability to extract general rules from the training data, and combine them to process new examples. As systematic compositionality allows very fast generalization to unseen cases, lack of compositional learning might be one root of RNNs training data thirst. I will next present a study where RNNs must solve an apparently simple task where correct generalization relies on function composition. The results suggest that a large random search in RNN space finds a small portion of models that converged to a (limited) compositional solution. Finally, if time allows, I will present ongoing work in which we study the compositional abilities of human subjects, trying to uncover the priors that subtend their generalization skills.


October 22: Marco Baroni "Tabula nearly rasa: Probing the linguistic knowledge of character-level neural language models trained on unsegmented text (work in collaboration with Michael Hahn)"

Slides

As recurrent neural networks (RNNs) have recently reached striking performance levels in a variety of natural language processing tasks, there has been a revival of interest in whether these generic sequence processing devices are effectively capturing linguistic knowledge. Nearly all studies of this sort, however, initialize the RNNs with a vocabulaty of known words, and feed them tokenized input during training. We are instead running an extensive, multi-lingual (English/German/Italian) study of the linguistic knowledge induced by RNNs trained at the character level on input data with whitespace removed. Our networks, thus, face a tougher and more cognitively realistic task, having to discover all the levels of the linguistic hierarchy from scratch. Our current results show that these "near tabula rasa" RNNs are implicitly encoding a surprising amount of phonological, lexical, morphological, syntactic and semantic information, opening the doors to intriguing speculations about the degree of prior knowledge that is necessary for succesful language learning.


October 17: Vlad Maraev "Towards KoS/TTR-based proof-theoretic dialogue management (joint work with: Jonathan Ginzburg (Université Paris Diderot), Staffan Larsson, Ye Tian (Amazon Research), Jean-Philippe Bernardy)"

Slides 

This paper presents the first attempt to implement a dialogue manager based on the KoS framework for dialogue context and interaction. We utilise our own proof-theoretic implementation of Type Theory with Records (TTR) and implement a basis dialogue that involves mutual greeting. We emphasize the importance of findings in dialogue theory for designing dialogue systems which we illustrate by sketching an account for question-answer relevance.


October 10: Mehdi Ghanimifard "Spatial Knowledge In Neural Language Models"

Slides

Understanding and generating spatial descriptions requires, among other things, knowledge about how objects are related geometrically. The wide usage of neural language models in different areas, including in generation of scene descriptions, motivates our study how spatial geometric knowledge is encoded in them. We first examine how spatial descriptions are attended by state of the art model of attention in CNNs. We argue that adaptive attention is good at predicting what the objects are but less good on how they relate geometrically. Then we explore different models of encoding explicit spatial information in an end-to-end scene description model. We summarize with the implications of this work for improving image captioning system.


October 3: Rasmus Blanck "A Compositional Bayesian Semantics for Natural Language"

Slides

We propose a compositional Bayesian semantics that interprets declarative sentences in a natural language by assigning them probability conditions. These are conditional probabilities that estimate the likelihood that a competent speaker would endorse an assertion, given certain hypotheses. Our semantics is implemented in a functional programming language. It estimates the marginal probability of a sentence through Markov Chain Monte Carlo (MCMC) sampling of objects in vector space models satisfying specified hypotheses. We apply our semantics to examples with several predicates and generalised quantifiers, including higher-order quantifiers. It captures the vagueness of predication (both gradable and non-gradable), without positing a precise boundary for classifier application. We present a basic account of semantic learning based on our semantic system. We compare our proposal to other current theories of probabilistic semantics, and we show that it offers several important advantages over these accounts.


May 16: Richard Sproat "Induction of Finite-State Covering Grammars for Text Normalization (joint work with Kyle Gorman)"

slides

In this talk I will introduce our work on applying neural methods to the problem of text normalization. Though the performance of the system overall is good, it is prone to what we term "silly errors", where for example, "2mA" is read as "two million liters". We have found that finite-state covering grammars are useful for mitigating such errors, and I will discuss induction of such covering grammars from data. I start with presenting our work on inducing grammars for number names (123 verbalized as one hundred (and) twenty three). This work draws inspiration from the (small) linguistics literature on number names, and our method allows one to train finite-state transducers with small amounts of data (a few hundred examples). I will compare the performance with that of an RNN trained on orders of magnitude more data. I will then report on our ongoing work on inducing grammars for a wider range of text normalization problems.


May 14: Richard Sproat "A computational model of the discovery of writing"

slides

This paper reports on a computational simulation of the evolution of early writing systems from pre-linguistic symbol systems, something for which there is poor evidence in the archaeological record. The simulation starts with a completely concept-based set of symbols, and then spreads those symbols and combinations of these to morphemes of artificially generated languages based on semantic and phonetic similarity.

While the simulation is crude, it is able to account for the observation that the development of writing systems ex nihilo seems to be facilitated in languages that have largely monosyllabic morphemes, or that have abundant ablauting processes. We are also able to model what appears to be two possible lines of development in early writing whereby symbols are associated to the sounds of all morphemes linked to a concept (as seems to have been the case in Sumerian), versus just one morpheme linked to a concept (as seems to have been the case in Chinese). Finally, the model is able to offer an account of the apparent rapid development of writing in Mesopotamia that obviates the need to posit a conscious invention of writing, as proposed by Jean-Jacques Glassner. The proposed model thus opens a new approach to thinking about the emergence of writing and its properties, something that, as noted above, has scant direct archaeological evidence.

The software is released open-source on GitHub.


March 15: Sam Bowman "Sentence Understanding with Neural Networks and Natural Language Inference"

Slides

Artificial neural networks now represent the state of the art in most large-scale applied language understanding tasks. This talk presents a few methods and results, organized around the task of recognizing textual entailment, which measure the degree to which these models can or do learn something resembling compositional semantics. I discuss experiments on artificial data and on a hand-built million-example corpus of natural data (SNLI/MultiNLI), and report encouraging results.

References
Bowman, Samuel R., Christopher Potts, and Christopher D. Manning. "Recursive neural networks can learn logical semantics." arXiv preprint arXiv:1406.1827 (2014).


March 12: Sam Bowman "Two Early Efforts toward Using Deep Learning in Syntax and Semantics"

Slides

This talk will present two ongoing projects that aim to lay the groundwork to use results from artificial neural networks research in NLP to inform research on core linguistic questions. The first project (based partially on WIlliams et al. 2017) concerns latent tree learning: efforts to discover the optimal tree structures for use in guiding semantic composition for applied language understanding tasks. The second concerns the evaluation of simple neural network models on the classic linguistic acceptability judgments task. This project (in progress, with Alex Warstadt) builds on Lau, Clark, and Lappin '16, and introduces a new dataset of expert acceptability judgments and a new suite of semi-supervised learning experiments with neural networks.

References
Williams, Adina, Andrew Drozdov, and Samuel R. Bowman. "Learning to parse from a semantic objective: It works. Is it syntax?." arXiv preprint arXiv:1709.01121 (2017).

Lau, Jey Han, Alexander Clark, and Shalom Lappin. "Grammaticality, acceptability, and probability: a probabilistic view of linguistic knowledge." Cognitive Science 41.5 (2017): 1202-1241.


March 8: Stephan Oepen "Holes in Meaning Construction with Minimal Recursion Semantics" (joint work with Dan Flickinger)

Slides

In joint work with Dan Flickinger, we provide a semi-formal review of
the meaning construction process in the English Resource Grammar
(ERG), which derives underspecified logical-form meaning
representations for a broad range of lexical classes and
syntactico-semantic constructions in English. We critically revise
and extend the proposal for an MRS algebra by Copestake et al. (2001;ACL) and speculate about the applicability of ERG-style meaning construction to the more coarse-grained syntactic analyses of enhanced Universal Dependencies.


March 8: Dag Haug "Glue semantics for Universal dependencies" (joint work with Matthew Gotham)

Slides

In this talk I explore the use of techniques from Glue semantics for
composing meaning representations based on Universal Dependencies (UD) syntactic structures. The UD tree is rewritten as meaning constructors consisting of a lambda term over DRSs and a linear logic formula that guides the semantic composition. Unlike many other frameworks, Glue semantics does not presuppose a one-to-one mapping from syntax to semantics, which is useful when dealing with highly underspecified syntactic representations of the UD kind.

2017

 

December 13: Rasmus Blanck "Rough Sets and Degree Modifiers"

Slides

Rough sets were introduced by Pawlak in 1982, as a generalisation of classical set theory. A rough set is characterised by its upper and lower approximation, respectively, the objects that might belong to the set, and the objects that surely belong to the set. Although this approach has some similarities with fuzzy set theory, the perceived fuzziness of rough
sets does not come from an underlying fuzzy logic, making rough sets a little less fuzzy than fuzzy sets.

In this talk, I will entertain the possibility that rough sets can be used to model degree modifiers. After an introduction to  rough set theory, I will briefly discuss its relation to fuzzy set theory, and point out some possible advantages of rough sets. Finally, I will reintroduce some fuzziness by generalising to probabilistic rough sets.


November 17th: Maxime Amblard "A Formal Account of Disorders in Dialogues"

Slides

This talk will present the project SLAM (Schizophrenia and Language - Analyse and Modelling). Since 2011, we build and analyse a corpus of interviews of patient with schizophrenia, in french.
Schizophrenia is well-known among mental illnesses for the severity of the thought disorders it involves, and for their widespread and spectacular manifestations ranging from deviant social behavior to delusion, not to mention affective and sensory distortions.
The goal of the SLAM project is twofold: (i) to discuss how the concepts of rationality and logicality may apply to conversational contexts in which one of the speakers is a patient with schizophrenia, and (ii) to use logical framework to model specific manifestations, namely disorders in conversational speech.

Our data are taken from transcriptions of real conversations between a psychologist and a patient with schizophrenia. Data collection and selection relied on theoretical hypotheses from psychiatry and psychopathology. Confronted with such a pathological conversation, any "ordinary" speaker intuitively feels that there are some incoherencies or discontinuities. We use a DRT (Kamp and Reyle 1993) like semantics in order to propose an interpretation model for such incongruities.

On our recent works, we focus on the extension of compositional semantics based on TTDL (de Groote 2006), a lambda-calculus with continuations. One of our research project is to develop TTDL for Dialogue, in the same perspective as TTR (Cooper and Ginzburg 2002, Cooper 2004, Cooper and Ranta 2008).
(Another one is the french translation of the Fracas resource, but it is not directly rely to SLAM)

The talk will present the SLAM corpus and project, and then (briefly) sketch the on-going works.

October 25: Aleksandre Maskharashvili "An Abstract Categorial Grammar Approach to the Discourse Modeling"

Slides

Various theories have been proposed in order to analyze a discourse in terms of rhetorical (discourse) relations. The main assumption in those theories is that every meaningful piece of a felicitous discourse is related to some piece of that discourse with a rhetorical relation. This gives rise to a notion of a rhetorical (discourse) structure. In order to analyze a discourse, both from the parsing and structural points of view, formal grammars of discourse, D-STAG and D-LTAG, were proposed. They build their discourse grammars on top of sentence-level grammars. Discourse connectives are main lexical means for expressing rhetorical relations. They play a similar role in discourse grammars as words do in sentence-level grammars. A discourse connective may appear inside a clause (a clause-medial position) or in front of a clause (a clause-initial position). The grammars of D-STAG and D-LTAG are capable of modeling cases where discourse connectives occupy only clause-initial positions. To process discourses where a discourse connective appears at a clause-medial position, D-STAG and D-LTAG make use of preprocessing of a discourse, which involves moving connectives from clause-medial positions to clause-initial ones. Afterwards, the grammars of D-STAG and D-LTAG can be employed to parse the discourse and simultaneously construct its rhetorical structure. Thus, D-STAG and D-LTAG, each makes use of a two-step process to analyze a discourse. We develop a single-step, purely grammatical approach for analyzing a discourse. Our framework is Abstract Categorial Grammars (ACGs). Our encoding falls into the class of second-order ACGs, which guarantees that the tasks of discourse parsing and generation are of polynomial complexity. In addition, our encoding puts together the discourse-level and sentence-level grammars within a single grammar. This makes our approach beneficial for reducing problems related to ambiguity that arise in the case of treating the discourse-level and sentence-level grammars separately.


September 13: Sharid Loaiciga "What is it? Disambiguating the different readings of the pronoun 'it'

Slides

Machine translation of pronouns is problematic for different reasons. Languages differ in their pronoun systems, creating mismatches in features like gender, number, case, formality, animacy and overtness. Another reason is functional ambiguity. Some pronouns have the same surface form but different functions. In this talk, I will address the problem of predicting one of three functions of the English pronoun 'it': anaphoric (e.g., 'The party ended late. It was fun.'), event reference (e.g., 'He can't speak Finnish. It annoys me.') and pleonastic (e.g., 'It's been raining all day.').

I will present experiments using a maximum entropy classifier (MaxEnt) trained on gold-standard data and self-training experiments with a recurrent neural network classifier (RNN) trained on silver standard data, annotated using the MaxEnt classifier. I will show an analysis demonstrating that these models, rather than one being better than the other, have different strengths. I will also present an example of the integration of source pronoun function into an n-gram language model used for target pronoun translation prediction.

The it-disambiguation task is valuable for MT but also for the field of coreference resolution. Standard coreference resolution systems focus on identifying nominal-referential instances, de facto grouping together and discarding the event and pleonastic categories. Linguistically, however, event instances are also referential. I will finish the talk by brainstorming some ideas about how to integrate my work into this field.


May 29: Judith Holler "On the pragmatics of face-to-face communication: the role of the body in social cognition and social interaction"

Slides

Coordination is at the heart of human conversation. In order to interact with one another through talk, we must coordinate at many levels, first and foremost at the level of our mental states, intentions and conversational contributions. In this talk, I will present findings on the pragmatics of multi-modal communication from both production and comprehension studies. In terms of production, I will, firstly, throw light on how co-speech gestures are used in the coordination of meaning to allow interactants to arrive at a shared understanding of the things they talk about, and, secondly, on how gesture and gaze are employed in the coordination of speaking turns in spontaneous conversation, with special reference to the psycholinguistic and cognitive challenges that turn-taking poses. In terms of comprehension, I will focus on communicative intentions and the interplay of ostensive and semantic multi-modal signals in triadic communication contexts. My talk will bring these different findings together to make the argument for richer research paradigms that capture more of the complexities and sociality of face-to-face conversational interaction. Advancing the field of multi-modal communication in this way will allow us to more fully understand the psycholinguistic processes that underlie human language use and language comprehension.


Slides

May 10: David Schlangen "Learning and Maintaining a Lexicon for Situated Interaction"

If, when asked to "point at the mug", a physically unimpaired person
seems unable to identify a potential referent that is standing in front
of them, we might hesitate to ascribe knowledge of the meaning of the
word "mug" to them, whatever else they may be able to tell us about mugs
(e.g., "wooden mugs were produced probably from the oldest time, but
most of them have not survived intact.", or "mugs are similar to cups").
And yet computational models of word meaning are good at the latter
(e.g., by simply linking to knowledge repositories like wikipedia, where
the previous sentence about wooden mugs was taken from), and fail at the
former.

In this talk, I will present our recent work at learning a lexicon for
referential interaction, where the referential aspects of word meaning
are modelled through perceptual classifiers taking real images as input.
I show that this representation complements other computational meaning
representations such as those derived from distributional patterns, as
well as decompositional or attribute-based representations. The lexicon
is learned through (observation of) interaction, and is maintained and
defended in interaction.


May 5th: Eve Clark: Language as (Graded) Expertise

Just as in the acquisition of other forms of expertise, learning a first language depends on three essential ingredients: exposure, practice, and feedback. Young children are exposed to the community language; they practice it in interaction with more expert speakers from around the age of 1, and they receive feedback on their practice. Fior example, adults check up on their errors with reformulations in the shape of side-sequences and embedded corrections. Adults also offer feedback on appropriate usage, ratifying the information being added to common ground. Finally, in L1 acquisition, children are learning just what one can and can't do with language, as they learn to understand and produce it. In L2 acquisition, learners typically receive much less exposure in interactive settings, receive less feedback timed to pinpoint specific errors, and have less opportunity for practice in truly interactive settings.


May 4th: Herbert Clark "Performing depictions in everyday discourse"

Depicting is a basic method of communication on a par with describing and pointing (or indicating). The idea is that people use their hands, arms, head, face, eyes, voice, and body, with and without props, to stage physical scenes for others, generally as composite parts of utterances along with describing and pointing. Performing depictions, I will show, is inherently interactive, and people choose depictions to communicate things they could not do with language or pointing.

May 3rd: Eve Clark "Why Interaction Promotes Language Acquisition"

Slides

Children acquire language as they interact with adults from infancy onwards. Adults-parents and caretakers-are 'expert speakers' and they guide children's earliest steps in interaction, from gaze, to smiles, to reaching, to attempting to communicate. Although very young children can communicate some things early on by pointing and reaching, or by pushing things away, the nonverbal options here are limited in scope. Language offers a lot more. But to acquire language, children need extensive exposure in interaction. In this talk, I will review some of the evidence for how children manage the complex feat of acquiring the basics of a language and how to use it, by around age four- but just the basics. The process of acquiring a language, for all the things we can learn to do with language, lasts a good deal longer.


May 2nd: Herbert H. Clark "On the rational basis of communication"

Communication is often said to be a rational behavior. As Grice (1975) put it, "Talking [is] a special case or variety of purposive, indeed rational behavior." But what does it mean for a behavior to be rational? I will contrast two notions of rationality as they have been applied to language use, one cooperative (à la Grice) and the other interactive, and argue that both are legitimate. I will show how the interactive model, based on one type of rationality, accounts for a wide range of phenomena that are complementary for those accounted for in a Gricean cooperative model. 


March 22: Eleni Gregoromichelaki "Ad hoc grammatical categorisation in Dynamic Syntax"

SlidesAbstract

The view of NLs as codes mediating a mapping between "expressions" and the world is abandoned to give way to a view where utterances are seen as actions aimed to locally  and incrementally alter the affordances of the context. Such actions employ perceptual stimuli  composed not only of "words" and "syntax" but also elements like visual marks, gestures,  sounds, etc. Any such stimuli can participate in the domain-general processes that constitute the "grammar", whose function is the dynamic categorisation of various perceptual inputs and their integration in the process  of generating the next action steps. Given these assumptions, a challenge that arises is how to account for the reification of such processes as exemplified in apparent metarepresentational practices like quotation, reporting, citation etc. It is argued that even such phenomena can receive adequate and natural explanations through a grammar that allows for
the ad hoc creation of occasion-specific content through reflexive mechanisms.


March 20: Hannes Rieser "A Process Algebra Account of Speech-Gesture Interaction"

Slides

The talk is based on extensive corpus work dealing with the interaction of gesture and speech in natural route-description dialogues. The issue discussed is how non-regimented gesture and speech processes can be modelled in a formal system. The main argument in the talk is that this cannot be achieved in structural paradigms currently in use. The proposal is to turn instead to process algebras in the tradition of Milner's pi-calculus. The special algebra discussed in the talk is a newly developed hybrid lambda-psi calculus which can transport typed lambda-expressions over communicating input-output channels. Central for the account is the notion of agent: Agents encode speech information, gesture information or both. They can put information on channels and send it to other channels or take information from others and combine it with the information they have. Speech-gesture interaction is conceptualised via channel interactions of this sort. Interactions are allowed, postponed or blocked using a typing system. Successful communication among agents leads to a multi-modal meaning representation serving as logical form.

Bibliography:
Rieser, H. (2014). Gesture and Speech as Autonomous Communicating Processes. Talk at the Stuttgart Workshop on "Embodied meaning goes public". Stuttgart University, December 2014
Rieser, H. (2015). When Hands Talk to Mouth. Gesture and Speech as Autonomous Communicating Processes. Proceedings of Semdial 2015, Gothenburg
Rieser, H. (2017). A Process Algebra Account of Speech-Gesture Interaction. Preliminary version. Ms, Bielefeld University


March 1: Kristina Liefke "Relating Theories of Formal Semantics: established methods and surprising results"

Slides

Formal semantics comprises a plethora of theories which interpret natural language through the use of di¿erent ontological primitives (e.g. possible worlds, situations, individual concepts, unanalyzable propositions). The ontological relations between these theories are, today, still largely unexplored. In particular, it remains unclear whether the basic objects of some of these theories can be coded in terms of objects from other theories (s.t. phenomena which are modeled by one theory can also be modeled by the other theories) or whether some of these theories can even be reduced to ontologically poor(er) theories (e.g. extensional semantics) which do not contain ¿special¿ objects like possible worlds.

This talk surveys my recent work on ontological reduction relations between formal semantic theories. This work shows that, more than preserving the modeling success of the reduced theory, some reductions even improve upon the theory's modeling adequacy or widen the theory's modeling scope. My talk illustrates this observation by two examples: (i) the relation between Montague-/possible world-style intensional semantics and extensional semantics (cf. Liefke and Sanders 2016), and (ii) the relation between intensional semantics and situationbased single-type semantics (cf. Liefke and Werning, in revision). The first relation established through the use of associates from higher-order recursion theory.


February 20: Asad Sayeed "Semantic representation and world knowledge"

SlidesWhile general knowledge of the world plays a role in language use,
language processing in humans is also guided by formal intuitions about
linguistic representation. In this talk, I discuss research results in finding the boundaries between world knowledge and formalism-driven intuitions and situate them in the context of a larger research program in computational psycholinguistics.

The first result focuses on the semantics of predicates and their arguments and how they are interpreted by the human processor. English-speaking human raters judge doctors as more appropriate givers of advice than recipients and lunches as much more appropriate objects of "eat" than subjects. One of my recent projects resulted in the development of vector-space and neural network models of predicate-argument relations that model that succeed in achieving high correlations with human ratings.

The second result is about the interaction of world knowledge with higher order semantics. English-speakers tend to judge that the sentence "every child climbed a tree" refers to more than one tree, while "every jeweller appraised a diamond" is comparatively more likely to refer to a single diamond, based on their knowledge of trees and diamonds. Recent experimental results in the literature are ambivalent on the extent to which formal structure affects the power of world knowledge to influence these judgements. In response to this, I describe a recent judgement study I conducted using German scrambling that suggests a significant effect of formal representation on the plural interpretation of an object argument given a universally-quantified subject.

Both of these research efforts reveal underlying questions about the
influence of world knowledge on linguistic representations and suggest
ways to answer them.


February 1: Mathieu Lafourcade "Games with a Purpose: The JeuxdeMots project"

Slides

Human-based computation is an approach where some steps of a computation is outsourced to humans. Games with a purpose (GWAPs) are games aiming at resolving puzzled or collecting data, where humans still outperform machines.

The JeuxDeMots (JDM) projects is a web-based associative GWAP where people are invited to play on various lexical and semantic relations between terms. The aim of this project is to build a large lexico-semantic network, with various relations types and word refinements (word usages).

Text semantic analysis is the main application for exploiting this resource, however the use as a tool for providing help in the case of the "tip of the tongue" phenomenon is also fruitful. This presentation will present the principles behind the JDM project, as well has the results achieved so far (around 1 million terms for 67 million relations). The following aspects will be discussed: the interaction between the various games of the JDM environment, some inference mechanisms of relations and rules, word polarity and sentiments, and some ethical aspects. Some specific aspects of the JDM lexical network are detailed, such as : refinements, aggregated terms, inhibitory relations and relation annotations.

References:

https://en.wikipedia.org/wiki/Human-based_computation
https://en.wikipedia.org/wiki/Human-based_computation_game
M. Lafourcade, N. Le Brun, and A. Joubert (2015) Games with a Purpose (GWAPS) ISBN: 978-1-84821-803-1 July 2015, Wiley-ISTE, 158 p.

M. Lafourcade, A. Joubert (2015) TOTAKI: A Help for Lexical Access on the TOT Problem. In Gala, N., Rapp, R. et Bel-Enguix, G. éds. (2015), Language Production, Cognition, and the Lexicon. Festschrift in honor of Michael Zock. Series Text, Speech and Language Technology XI. Dordrecht, Springer. 586 p. 140 illus. ISBN: 978-3-319-08042-0. (pp. 95-112)


2016

 

November 30: Peter Sutton "A probabilistic, mereological account of the mass/count distinction"

Slides

In this paper, we attempt to answer the vexing question why it should be the case that only certain types of noun meanings exhibit mass/count variation in the lexicalization of their semantic properties, while others do not. This question has so far remained unanswered, or been set aside. We will do so by focusing on the role of context-sensitivity (already highlighted in recent theories of the mass/count distinction), and argue that it gives rise to a conflict between two pressures that influence the encoding of noun meanings as mass or count, one stemming from learnability constraints (reliability) and the other from constraints on informativeness (individuation). This will also lead us to identifying four semantic classes of nouns, and to showing why variation in mass/count encoding is, on our account, to be expected to occur widely in just two of them. Context-sensitivity forces a choice between prioritizing individuation, which aligns with count lexicalization, and prioritizing consistency, which aligns with mass lexicalization.


November 14: Jean-Philippe Bernandy "Efficient Parallel and Incremental Parsing of Practical Context-Free Languages"

Slides

We present a divide-and-conquer algorithm for parsing
context-free languages efficiently. Our algorithm is an instance
of Valiant's (1975), who reduced the problem of parsing to matrix
multiplications. We show that, while the conquer step of
Valiant's is O(n³), it improves to O(log² n) under certain
conditions satisfied by many useful inputs.

One observes that inputs written by humans generally satisfy
those conditions. Thus, there appears to be a link between the
ability for a computer to efficiently parse an input in parallel
and the ability for a human to comprehend such an input.


November 2: Ruth Kempson "Language: The Tool for Interaction -- Surfing Uncertainty Together"

Slides

With established recognition of the endemic context-relativity of language, it is now generally accepted that both parsing and production involve incremental context-relative decisions, requiring the concepts of both evolving contents and evolving contexts. Researchers across semantics, pragmatics, psycholinguistics, and computational linguistics are duly turning to the challenge of modelling language in terms that are compatible with such incrementality. Yet formal models of language remain largely grounded in the static terms of licensing sentential string- interpretation pairings reflecting only concepts such as compositionality, with little or no reflection of a time-linear process of information growth.

In this talk, I start by showing why linguists cannot avoid the challenge of defining grammar formalisms to reflect the dynamics of conversational dialogue, and how in order to achieve this, every aspect of linguistic knowledge needs to be recast as procedures for on-line incremental and predictive word-by-word understanding/production. I shall then briefly sketch the action-based Dynamic Syntax (DS) system to demonstrate its explanatory potential, by modelling what have been taken as canonical exemplars of semantic-independent syntactic processes, which in DS are all expressed in terms of incremental parsing/generation actions. I will show in passing how the resulting system, despite the lack of any conventional notion of syntax, nonetheless has the power to express both universal structural constraints and yet cross-language variability. Part of this will include the Directed Acyclic Graph characterisation of context as developing in lockstep with the evolving yet revisable content, demonstrating the system-internal potential for self/other-correction. The dynamics of conversational dialogue interactions will then emerge as the immediate consequence of this perspective on language; and I will briefly illustrate how this potential for interaction underpins all types of language-internal licensing constraint: syntactic, semantic, morphosyntactic and phonological.

I shall then turn to setting this perspective within the Predictive Processing (PP) model of cognition (Clark 2016), whose architectural properties the DS concept of language matches almost point by point. Like perception in the PP model, the DS grammar is a "fundamentally action-oriented" set of procedures, grounded in predictive processing resources shared by speakers (action) and hearers (perception) alike and "executed using the same basic computational strategy" leading to effects of interactive coordination without any need to invoke mind-reading or propositional inference. The result is that linguistic processing, perception, action, and thought are predicted to be "continuously intermingled" yielding representational updates "tailored to good enough online controls rather than aiming for rich mirroring". Instead, such updates are accomplished due to a strong version of affordance competition since the brain ¿continuously computes multiple probabilistically inflected possibilities for action¿ in a cost-effect balancing dynamic, with possibilities progressively winnowed down, allowing for possible revision, to yield at least one output in any successful outcome. To this set of characteristics (Clark 2016 p. 251), we have only to add the potential for interaction which such a language system predicts as default, and a wholly different perspective on language evolution opens up. Language can now be seen as an emergent and evolving system with manifest potential for consolidating cross-individual interactions, hence group effects, without ever having to invoke high-level inferences as external, "designer"-imposed motivation for such consolidation, this a dynamic for which language change already provides robust motivation.


October 18: Matthew Stone "A Bayesian model of grounded color semantics"

Slides

Natural language meanings allow speakers to encode important real-world distinctions, but corpora of grounded language use also reveal that speakers categorize the world in different ways and describe situations with different terminology. To learn meanings from data, we therefore need to link underlying representations of meaning to models of speaker judgment and speaker choice. This paper describes a new approach to this problem: we model variability through uncertainty in categorization boundaries and distributions over preferred vocabulary. We apply the approach to a large data set of color descriptions, where statistical evaluation documents its accuracy. The results are available as a Lexicon of Uncertain Color Standards (LUX), which supports future efforts in grounded language understanding and generation by probabilistically mapping 829 English color descriptions to potentially context-sensitive regions in HSV color space.

joint work with Brian McMahan.


September 13: Ev Fedorenko "The internal architecture of the language network"

Slides

A set of brain regions on the lateral surfaces of left frontal, temporal, and parietal cortices robustly respond during language comprehension and production. Although we now have strong evidence that this language network is spatially and functionally distinct from brain networks that support other high-level cognitive functions, the internal structure of the language network remains poorly understood. Deciphering the language network's architecture includes i) identifying its component parts, and ii) understanding the division of labor among those components in space and time. I will first present evidence that all language regions closely track linguistic input. I will then argue that some of the traditional "cuts" that have been proposed in the literature (e.g., based on the size of the linguistic units, based on the distinction between storage and computation, or based on syntactic category) do not seem to be supported by the available evidence. Even aspects of language that have long been argued to preferentially, or selectively, rely on a specific region within the language network (e.g., syntactic processing being localized to parts of Broca¿s area) appear to be distributed across the network. Further, the very same regions that are sensitive to syntactic structure in language show sensitivity to lexical and phonological manipulations. This distributed nature of language processing is in line with much current theorizing in linguistics and the available behavioral psycholinguistic data that show sensitivity to contingencies spanning sound-, word- and phrase-level structure. Time permitting, I will talk about recent work on decoding single word meanings and more complex meanings from the neural activity in the language network, and speculate that the organizing principles of the language network may have to do with meaning.

Relevant readings:
https://evlab.mit.edu/sites/default/files/documents/Fedorenko_et_al_2012_Nplogia.pdf
https://evlab.mit.edu/sites/default/files/documents/Blank_et_al_2016.pdf


September 12: Ted Gibson "Information processing and cross-linguistic universals"
 

Slides

Finding explanations for the observed variation in human languages is the primary goal of linguistics, and promises to shed light on the nature of human cognition. One particularly attractive set of explanations is functional in nature, holding that language universals are grounded in the known properties of human information processing. The idea is that grammars of languages have evolved so that language users can communicate using sentences that are relatively easy to produce and comprehend. In this talk, I summarize results from explorations in two linguistic domains, from an information-processing point of view.

First, I consider communication-based origins of lexicons of human languages. Chomsky has famously argued that this is a flawed hypothesis, because of the existence of such phenomena as ambiguity. Contrary to Chomsky, we show that ambiguity out of context is not only not a problem for an information-theoretic approach to language, it is a feature. Furthermore, word lengths are optimized on average according to predictability in context, as would be expected under an information theoretic analysis. We then apply this simple information-theoretic idea to a well-studied semantic domain: words for colors. And finally, I show that all the world's languages that we can currently analyze minimize syntactic dependency lengths to some degree, as would be expected under information processing considerations.

Readings:

Piantadosi, S.T., Tily, H. & Gibson, E. (2012). The communicative function of ambiguity in language.Cognition 122: 280-291.
http://tedlab.mit.edu/tedlab_website/researchpapers/Piantadosi_et_al_2012_Cogn.pdf

Piantadosi, S.T., Tily, H. & Gibson, E. (2011). Word lengths are optimized for efficient communication.Proceedings of the National Academy of Sciences 108(9): 3526-3529.
http://tedlab.mit.edu/tedlab_website/researchpapers/Piantadosi_et_al_2011_PNAS.pdf

Futrell, R., Mahowald, K., & Gibson, E. (2015). Large-scale evidence of dependency length minimization in 37 languages. Proceedings of the National Academy of Sciences 112(33): 10336-10341. doi: 10.1073/pnas.1502134112.
http://tedlab.mit.edu/tedlab_website/researchpapers/Futrell_et_al_2015_PNAS.pdf


September 8: Ev Fedorenko "The human language network within the broader architecture of the human mind and brain"

 Link to the recorded talk

 Slides

Although many animal species have the ability to generate complex thoughts, only humans can share such thoughts with one another, via language. My research aims to understand i) the system that supports our linguistic abilities, including its neural implementation, and ii) the relationship between the language system and the rest of the human cognitive arsenal. I use behavioral, fMRI, and genotyping methods in healthy adults and children, intracranial recordings from the cortical surface in patients undergoing pre- or intra-surgical mapping (ECoG), and studies of individuals with developmental and acquired damage.


I will begin by introducing the "language network", a set of interconnected brain regions that support language comprehension and production. With a focus on the subset of this network dedicated to high-level linguistic processing, I will then consider the relationship between language and non-linguistic cognition. Based on data from fMRI studies and investigations of patients with severe aphasia, I will argue that the language network is functionally selective for language processing over a wide range of non-linguistic processes that have been previously argued to share computational demands with language, including arithmetic, executive functions, music, and action/gesture observation. This network plausibly stores our linguistic knowledge, which can be used for both interpreting and generating linguistic utterances. Time permitting, I will speculate on the relationship between the language network and other networks, including, critically, the domain-general executive system, and the system that supports social cognition.

Relevant readings:
https://evlab.mit.edu/sites/default/files/documents/Fedorenko_et_al_2011_PNAS.pdf
https://evlab.mit.edu/sites/default/files/documents/Blank_et_al_2014_JNeurophys.pdf
https://evlab.mit.edu/sites/default/files/documents/Fedorenko_%26_Varley_2016_ANYAS.pdf


September 6: Ted Gibson "Language processing over a noisy channel"

Link to the recorded talk

Slides

Traditional linguistic models of syntax and language processing have assumed an error-free process of language transmission. But we know that this is not the case: people often make errors in both language production and comprehension. This has important ramifications for both models of language processing and language evolution. I first show that language comprehension appears to function as a noisy channel process, in line with communication theory.  Given si, the intended sentence, and sp, the perceived sentence we propose that people maximize P(si | sp ), which is equivalent to maximizing the product of the prior P(si) and the likely noise processes P(si → sp ).  I show how this simple formulation can explain a wide range of language processing phenomena, such as people’s interpretations of simple sentences, some aphasic language comprehension effects, and the P600 in the ERP literature. Finally, I discuss how thinking of language as communication in this way can explain aspects of the origin of word order, most notably that most human languages are SOV with case-marking, or SVO without case-marking.

Readings:

Gibson, E., Bergen, L. & Piantadosi, S. (2013). The rational integration of noisy evidence and prior semantic expectations in sentence interpretation. Proceedings of the National Academy of Sciences, 110(20): 8051-8056. doi: 10.1073/pnas.1216438110.
http://tedlab.mit.edu/tedlab_website/researchpapers/Gibson_et_al_2013_PNAS

Gibson, E., Piantadosi, S., Brink, K., Bergen, L., Lim, E. & Saxe, R. (2013). A noisy-channel account of cross-linguistic word order variation. Psychological Science, 4(7): 1079-1088. doi: 10.1177.
http://tedlab.mit.edu/tedlab_website/researchpapers/Gibson_et_al_2013_PsychSci.pdf

Gibson, E., Sandberg, C., Fedorenko, E., Bergen, L., & Kiran, S. (2015). A rational inference approach to aphasic language comprehension. Aphasiology. doi: 10.1080/02687038.2015.1111994.
http://tedlab.mit.edu/tedlab_website/researchpapers/Gibson_et_al_Aphasiology_2015.pdf


June 16: Carla Umbach "Ad-hoc Kind-formation by Similarity"

Slides

Abstract:
The talk focuses on demonstratives of manner, quality and/or degree, like German "so", Polish "tak", and English "such" (mqd demonstratives). These demonstratives modify (some or all of) verbal, nominal and degree expressions. They can be used deictically and anaphorically, and may also occur as correlatives in equative comparison constructions. The example in (1) shows German "so" used deictically.

(1) a. (speaker pointing to someone dancing): So tanzt Anna auch. 'Anna dances like this, too.' -- manner
b. (speaker pointing to a table): So einen Tisch hat Anna auch. 'Anna has such a table / a table like this, too.' -- quality
c. (speaker pointing to a person): So groß ist Anna auch. 'Anna is this tall, too.' -- degree

A semantic interpretation of mqd demonstratives will be proposed starting from the intuition that there is a deictic component and a similarity component involved ¿ in all of (1a-c), the meaning of "so" can be paraphrased by "like this". The basic idea is that mqd demonstratives generate a class of items similar to the target of the pointing gesture, e.g., in (1b) a class of tables similar to the table the speaker points at. This interpretation accounts for fact that mqd demonstratives are directly referential differing from regular demonstratives only in expressing similarity instead of identity. Moreover, it accounts for their cross-categorical distribution.

The suggested analysis is compatible with Carlson's (1980) interpretation of English "such" as a kind- referring expression. In the case of quality and of manner similarity classes will be shown to behave like kinds, although they need not be previously given but are instead ad-hoc generated. In the case of degree, however, it will be argued (contra Anderson and Morzycki 2015) that the resulting similarity class does not establish a kind. In (1c) for example, the class of persons similar in height to the one pointed at does not exhibit kind-like behavior.
The similarity interpretation of mqd demonstratives includes three major research topics:

(i) the implementation of the similarity relation, which is done with the help of multidimensional
attribute spaces
(ii) the ad-hoc generation of kinds by similarity, which is shown experimentally to be restricted to
particular features of comparison, and
(iii) the interpretation of equative comparison constructions based on similarity classes.

In the talk, the focus will be on the second topic.

Anderson, C., and M. Morzycki (2015) Degrees as kinds. Natural Language and Linguistic Theory.
Carlson, G. (1980) Reference to kinds in English. New York and London: Garland.
Gust, H. & C.Umbach (2015) Making use of similarity in referential semantics. In H. Christiansen, I. Stojanovic,
G. Papadopoulos (eds.) 9th Conference on Modeling and Using Context, Context 2015, LNCS Springer. Umbach, C., & H. Gust (2014) Similarity Demonstratives. Lingua 149, 74-93.


May 12: Simon Dobnik "A Model for Attention-Driven Judgements in Type Theory with Records"

 Slides

Abstract:

Joint work with John D. Kelleher, Dublin Institute of Technology, Ireland

Type Theory with Records (TTR) has been proposed as a formal representational framework and a semantic model for embodied agents participating in situated dialogues (Dobnik et al., 2014). Although TTR has many potential advantages as a semantic model for embodied agents, one problem it faces is the combinatorial explosion of types that is implicit in the framework due to the fact that new types can be created dynamically by composing existing types.

A consequence of this combinatorial explosion is that the agent is left with an intractable problem of deciding which types to assign to perceptual data. The term judgement is the technical term used in TTR to describe the assignment of a type to perceptual data that in practice would be implemented as a sensory classification.

This paper makes 3 contributions to the discussion on the applicability of TTR to embodied agents. First, it highlights the problem of the combinatorial explosion of type assignment in TTR. Second, it presents a judgement control mechanism, based on the Load Theory of selective attention and cognitive control (Lavie et al., 2004), that addresses this problem. Third, it presents a computational framework, based on POMDPs (Kaelbling et al., 1998), that offers a basis for future practical experimentation on the feasibility of the proposed approach.

Lecturer:

Simon Dobnik is Senior Lecturer at the University of Gothenburg


May 4: Shay Cohen "Latent-Variable Grammars and Natural Language Semantics"


Slides

Abstract:

Probabilistic grammars are an important model family in natural language processing. They are used in the modeling of many problems, mostly prominently in syntax and semantics. Latent-variable grammars are an extension of vanilla probabilistic grammars, introducing latent variables that inject additional information into the grammar by using learning algorithms in the incomplete data setting.

In this talk, I will discuss work aimed at the development of (four) theoretically-motivated algorithms for the estimation of latent-variable grammars. I will discuss how we applied them to syntactic parsing, and more semantically-oriented problems such as machine translation, conversation modeling in online forums and question answering.

Lecturer:
Shay Cohen is a Chancellor's Fellow at the Institute for Language, Cognition and Computation, University of Edinburgh.


April 27: Zhaohui Luo "MTT-semantics Is Both Model-theoretic and Proof-theoretic"

Slides

Abstract:

In this talk, after briefly introducing the formal semantics in modern type theories (MTT-semantics), I shall argue that it is both model-theoretic and proof-theoretic. This is due to the unique features of MTTs: they contain rich type structures that provide powerful representational means (e.g., to represent collections as types) and, at the same time, are specified proof-theoretically as rule-based systems whose sentences (judgements) can be understood inferentially.

Considered in this way, MTTs arguably have promising advantages when employed as foundational languages for formal semantics, both theoretically and practically.

Lecturer:

Zhaohui Luo is a Professor of Computer Science at Royal Holloway, University of London. 


April 7: Staffan Larsson "Bayesian nets in probabilistic TTR"

Slides

Abstract:

There is a fair amount of evidence indicating that language acquisition in general crucially relies on probabilistic learning. It is not clear how a reasonable account of semantic learning could be constructed on the basis of the categorical type systems that either classical or revised semantic theories assume. We present probabilistic TTR (Cooper et al 2014) that makes explicit the assumption, common to most probability theories used in AI, that probability is distributed over situation types, rather than over sets of worlds. Improving on and going beyond Cooper et al (2014), we formulate elementary Bayesian classifiers (which can be modelled as two-layer Bayesian networks) in probabilistic TTR and use these to illustrate how our type theory serves as an interface between perceptual judgement, semantic interpretation, and semantic leaning. We also show how this account can be extended to cover general Bayesian nets.

Lecturer: 

Staffan Larsson is a professor of computational linguistics at CLASP.


March 16: Graeme Hirst "Who decides what a text means? (And what the answer implies for computational linguistics)"

Slides

Abstract:

Writer-based and reader-based views of text-meaning are reflected by the respective questions "What is the author trying to tell me?" and "What does this text mean to me personally?" Contemporary computational linguistics, however, generally takes neither view; applications do not attempt to answer either question.

Instead, a text is regarded as an object that is independent of, or detached from, its author or provenance, and as an object that has the same meaning for all readers. This is not adequate, however, for the further development of sophisticated NLP applications for intelligence gathering and question answering, let alone interactive dialog systems.

I will review the history of text-meaning in computational linguistics, discuss different views of text-meaning from the perspective of the needs of computational text analysis, and then extend the analysis to include discourse as well -in particular, the collaborative or negotiated construction of meaning and repair of misunderstanding.

Lecturer:
Graeme Hirst's research interests cover a range of topics in applied computational linguistics and natural language processing, including lexical semantics, the resolution of ambiguity in text, the analysis of authors' styles in literature and other text (including plagiarism detection and the detection of online sexual predators), identifying markers of Alzheimer's disease in language, and the automatic analysis of arguments and discourse (especially in political and parliamentary texts).


March 11: John Kelleher "Attention Models in Deep Learning for Machine Translation"

Slides

Abstract:
In the last number of years deep learning models have made a significant impact across a range of fields. Machine Translation is one such area of research. The development of the encoder-decoder architecture and its extension to include an attention mechanism has led to deep learning models achieving state of the art MT results for a number of langauge pairs.

However, an open question in deep learning for MT is what is the best attention mechanism to use. This talk will begin by reviewing the current state of the art in deep learning for MT. The second half of the talk will present a novel attention based encoder-decoder architecture for MT. This novel architecture is the result of collaborative research between John Kelleher, Giancarlo Salton, and Robert J. Ross.

Lecturer:
John Kelleher is a lecturer in the School of Computing at the Dublin Institute of Technology and a researcher at the Adapt research center. He currently supervises research projects in a number of areas including machine translation, activity recognition and discovery, dialogue systems, computational models of spatial language, and music transcription.

For the last number of years the majority of his research has used a machine learning methodology, and in 2015 he published a textbook on machine learning with MIT Press. John's collaborators on this research are Giancarlo Salton, who is a PhD student at the Dublin Institute of Technology, and Robert Ross who is a senior lecturer in the School of Computing at the Dublin Institute of Technology.


March 9: Stergios Chatzikyriakidis "Modern Type Theoretical Semantics: Reasoning Using Proof-Assistants"

Slides

Abstract: 

In this talk, I will discuss the use of Modern Type Theoretical Semantics  (MTTs) , i.e. type theories within the tradition of Martin Löf (1974, 1981), for reasoning about natural language semantics. I will first present a brief introduction of the features that make MTTs an attractive formal language to interpret NL semantics to. In particular, I will discuss a number of issues that have been successfully dealt with using MTTs like adjectival/adverbial modification, copredication and intensionality among other things.

Then, I will argue that the proof-theoretic nature of MTTs, i.e. the fact that they are proof-theoretically specified, in combination with their expresiveness makes them fit to perform reasoning tasks. This proof-theoretic aspect of MTTs has been the main reason that a number of proof-assistants implement variants of MTTs. One such proof-assistant, Coq, will be used as a way to show the applicability of MTTs in dealing with Natural Language Inference (NLI).

Firstly, I will show how NL semantics can be implemented in Coq and
then I will present how one can use Coq in order to reason with these
semantics. I will draw examples from the FraCas test suite platform in order to show the predictions the implemented semantics make as regards inference. I will then discuss issues like coverage and proof-automation and a number of ideas for future work, like extracting type ontologies from GWAP lexical networks and creating a parser/translator that will translate between English (or any other language) and the syntax of Coq.

I will end the talk by discussing the potential use of Coq implementing other semantic frameworks, like Montague Semantics, Davidsonian semantics and eventually a discussion on how Coq can be used with TTR (or even ProbTTR).

Lecturer:

Stergios Chatzikyriakidis is a researcher and research coordinator of CLASP. 


February 22: Jan van Eijck "Modelling Legal Relations"

Slides
Abstract: 

Jan van Eijck, CWI and ILLC, Amsterdam (http://homepages.cwi.nl/~jve/)

(joint work with Fengkui Ju, Beijing Normal University, Beijing, China)

We use propositional dynamic logic and ideas about propositional control
from the agency literature to construct a simple model of how legal
relations interact with actions that change the world, and with actions
that change the legal relations.

This work is relevant for attempts to construct restricted fragments of
natural language for legal reasoning that could be used in the creation of
(more) formal versions of legal documents suitable for `legal knowledge bases'.


February 18: Charalambos Themistocleous "Doing Type Theory in R"

Slides

Abstract:

In this talk, I will present R language (or simply R ), a dynamic, lazy, functional, programming language that was designed in 1993 by Ross Ihaka and Robert Gentleman. R adopts the underlying evaluation model of Scheme with the syntax of S, (a programming language, which was developed by John Chambers at Bell Laboratories).

R is an open-source programming language and the flexible statistical analysis toolkit implemented in R , made it the lingua franca for doing statistics. The R package repository (CRAN) features 7861 available packages, which extent the language. Also, there are guides on CRAN that group sets of R packages and functions by type of analysis, fields, or methodologies (e.g. Bayesian Inference, Probability Distributions, Machine Learning, Natural Language Processing).

The statistical capabilities of R along with its functional capabilities can transform R into a rich environment for doing Type Theory. Thus, I will conclude this talk by discussing possible extensions of R for A Probabilistic Rich Type Theory for Semantic Interpretation (Cooper, Dobnik, Lappin, and Larsson, 2015).

Lecturer:

Charalambos Themistocleous is a post-doc at CLASP. 
 

 

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