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arXiv · 1703.03130

A Structured Self-attentive Sentence Embedding

Zhouhan Lin·Minwei Feng·Cicero Nogueira dos Santos·Mo Yu·Bing Xiang·Bowen Zhou·Yoshua Bengio
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Abstract

This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification, and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks.

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Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio. 2017-03-09. A Structured Self-attentive Sentence Embedding. https://arxiv.org/abs/1703.03130

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