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        <title>Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast</title>
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        <namePart type="given">Liang</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Xuanjing</namePart>
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    <abstract>In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product. Pre-trained language models are fine-tuned with the translation ranking task. Existing work (Feng et al., 2020) uses sentences within the same batch as negatives, which can suffer from the issue of easy negatives. We adapt MoCo (He et al., 2020) to further improve the quality of alignment. As the experimental results show, the sentence representations produced by our model achieve the new state-of-the-art on several tasks, including Tatoeba en-zh similarity search (Artetxe andSchwenk, 2019b), BUCC en-zh bitext mining, and semantic textual similarity on 7 datasets.</abstract>
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%0 Conference Proceedings
%T Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast
%A Wang, Liang
%A Zhao, Wei
%A Liu, Jingming
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F wang-etal-2021-aligning
%X In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product. Pre-trained language models are fine-tuned with the translation ranking task. Existing work (Feng et al., 2020) uses sentences within the same batch as negatives, which can suffer from the issue of easy negatives. We adapt MoCo (He et al., 2020) to further improve the quality of alignment. As the experimental results show, the sentence representations produced by our model achieve the new state-of-the-art on several tasks, including Tatoeba en-zh similarity search (Artetxe andSchwenk, 2019b), BUCC en-zh bitext mining, and semantic textual similarity on 7 datasets.
%R 10.18653/v1/2021.emnlp-main.309
%U https://aclanthology.org/2021.emnlp-main.309
%U https://doi.org/10.18653/v1/2021.emnlp-main.309
%P 3807-3815

Markdown (Informal)

[Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast](https://aclanthology.org/2021.emnlp-main.309) (Wang et al., EMNLP 2021)

• Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast (Wang et al., EMNLP 2021)

ACL

• Liang Wang, Wei Zhao, and Jingming Liu. 2021. Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3807–3815, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.