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    <abstract>Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages. We propose a novel unsupervised method to derive multilingual sentence embeddings relying only on monolingual data. We first produce a synthetic parallel corpus using unsupervised machine translation, and use it to fine-tune a pretrained cross-lingual masked language model (XLM) to derive the multilingual sentence representations. The quality of the representations is evaluated on two parallel corpus mining tasks with improvements of up to 22 F1 points over vanilla XLM. In addition, we observe that a single synthetic bilingual corpus is able to improve results for other language pairs.</abstract>
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%0 Conference Proceedings
%T Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining
%A Kvapilíková, Ivana
%A Artetxe, Mikel
%A Labaka, Gorka
%A Agirre, Eneko
%A Bojar, Ondřej
%Y Rijhwani, Shruti
%Y Liu, Jiangming
%Y Wang, Yizhong
%Y Dror, Rotem
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F kvapilikova-etal-2020-unsupervised
%X Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages. We propose a novel unsupervised method to derive multilingual sentence embeddings relying only on monolingual data. We first produce a synthetic parallel corpus using unsupervised machine translation, and use it to fine-tune a pretrained cross-lingual masked language model (XLM) to derive the multilingual sentence representations. The quality of the representations is evaluated on two parallel corpus mining tasks with improvements of up to 22 F1 points over vanilla XLM. In addition, we observe that a single synthetic bilingual corpus is able to improve results for other language pairs.
%R 10.18653/v1/2020.acl-srw.34
%U https://aclanthology.org/2020.acl-srw.34
%U https://doi.org/10.18653/v1/2020.acl-srw.34
%P 255-262

Markdown (Informal)

[Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining](https://aclanthology.org/2020.acl-srw.34) (Kvapilíková et al., ACL 2020)

• Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining (Kvapilíková et al., ACL 2020)

ACL

• Ivana Kvapilíková, Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Ondřej Bojar. 2020. Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 255–262, Online. Association for Computational Linguistics.