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arXiv · 1804.09779

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

Adam Poliak·Yonatan Belinkov·James Glass·Benjamin Van Durme
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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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Adam Poliak, Yonatan Belinkov, James Glass, Benjamin Van Durme. 2018-04-25. On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference. https://arxiv.org/abs/1804.09779

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