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arXiv · 1812.11559

Variational Self-attention Model for Sentence Representation

Qiang Zhang·Shangsong Liang·Emine Yilmaz
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Abstract

This paper proposes a variational self-attention model (VSAM) that employs variational inference to derive self-attention. We model the self-attention vector as random variables by imposing a probabilistic distribution. The self-attention mechanism summarizes source information as an attention vector by weighted sum, where the weights are a learned probabilistic distribution. Compared with conventional deterministic counterpart, the stochastic units incorporated by VSAM allow multi-modal attention distributions. Furthermore, by marginalizing over the latent variables, VSAM is more robust against overfitting. Experiments on the stance detection task demonstrate the superiority of our method.

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BibTeXRIS

Qiang Zhang, Shangsong Liang, Emine Yilmaz. 2018-12-30. Variational Self-attention Model for Sentence Representation. https://arxiv.org/abs/1812.11559

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