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        <title>Self-Attention with Cross-Lingual Position Representation</title>
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        <namePart type="given">Liang</namePart>
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        <namePart type="given">Longyue</namePart>
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            <namePart type="given">Dan</namePart>
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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, 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 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\RightarrowGerman, WAT’17 Japanese\RightarrowEnglish, and WMT’17 ChineseŁeftrightarrowEnglish 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.</abstract>
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%0 Conference Proceedings
%T Self-Attention with Cross-Lingual Position Representation
%A Ding, Liang
%A Wang, Longyue
%A Tao, Dacheng
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ding-etal-2020-self
%X 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, 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 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\RightarrowGerman, WAT’17 Japanese\RightarrowEnglish, and WMT’17 ChineseŁeftrightarrowEnglish 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.
%R 10.18653/v1/2020.acl-main.153
%U https://aclanthology.org/2020.acl-main.153
%U https://doi.org/10.18653/v1/2020.acl-main.153
%P 1679-1685

Markdown (Informal)

[Self-Attention with Cross-Lingual Position Representation](https://aclanthology.org/2020.acl-main.153) (Ding et al., ACL 2020)

• Self-Attention with Cross-Lingual Position Representation (Ding et al., ACL 2020)

ACL

• Liang Ding, Longyue Wang, and Dacheng Tao. 2020. Self-Attention with Cross-Lingual Position Representation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1679–1685, Online. Association for Computational Linguistics.