Research Area:  Machine Learning
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.
Neural Machine Translation
Natural Language Inference
Author(s) Name:  Adam Poliak, Yonatan Belinkov, James Glass, Benjamin Van Durme
Journal name:  Computer Science
Publisher name:  arXiv:1804.09779
Paper Link:   https://arxiv.org/abs/1804.09779