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arXiv · 1901.06283

Improving Sequence-to-Sequence Learning via Optimal Transport

Liqun Chen·Yizhe Zhang·Ruiyi Zhang·Chenyang Tao·Zhe Gan·Haichao Zhang·Bai Li·Dinghan Shen·Changyou Chen·Lawrence Carin
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Abstract

Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word given the previous ground-truth partial sentence. This procedure focuses on modeling local syntactic patterns, and may fail to capture long-range semantic structure. We present a novel solution to alleviate these issues. Our approach imposes global sequence-level guidance via new supervision based on optimal transport, enabling the overall characterization and preservation of semantic features. We further show that this method can be understood as a Wasserstein gradient flow trying to match our model to the ground truth sequence distribution. Extensive experiments are conducted to validate the utility of the proposed approach, showing consistent improvements over a wide variety of NLP tasks, including machine translation, abstractive text summarization, and image captioning.

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Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen, Lawrence Carin. 2019-01-18. Improving Sequence-to-Sequence Learning via Optimal Transport. https://arxiv.org/abs/1901.06283

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