Self-Attention with Cross-Lingual Position Representation

Liang Ding; Longyue Wang; Dacheng Tao

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arXiv · 2004.13310

Self-Attention with Cross-Lingual Position Representation

Liang Ding·Longyue Wang·Dacheng Tao

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Abstract

Position encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences. However, in cross-lingual scenarios, e.g. machine translation, the PEs of source and target sentences are modeled independently. Due to word order divergences in different languages, modeling the cross-lingual positional relationships might help SANs tackle this problem. In this paper, we augment SANs with \emph{cross-lingual position representations} to model the bilingually aware latent structure for the input sentence. Specifically, we utilize bracketing transduction grammar (BTG)-based reordering information to encourage SANs to learn bilingual diagonal alignments. Experimental results on WMT'14 English$\Rightarrow$German, WAT'17 Japanese$\Rightarrow$English, and WMT'17 Chinese$\Leftrightarrow$English translation tasks demonstrate that our approach significantly and consistently improves translation quality over strong baselines. Extensive analyses confirm that the performance gains come from the cross-lingual information.

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Liang Ding, Longyue Wang, Dacheng Tao. 2020-04-28. Self-Attention with Cross-Lingual Position Representation. https://arxiv.org/abs/2004.13310

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