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

Research profile seminar

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

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.

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

Date: 3/15/2018

Time: 10:15 AM - 12:00 PM

Categories: Linguistics

Location: T307, Olof Wijksgatan 6

Contact person: stergios chatzikyriakidis

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