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On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference

2018-05-06 17:58:48
Adam Poliak, Yonatan Belinkov, James Glass, Benjamin Van Durme
arXiv_CL
           
Abstract

We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world-knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage.

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URL

https://arxiv.org/abs/1804.09779

PDF

https://arxiv.org/pdf/1804.09779.pdf

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