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        <title>Multi-Source Text Classification for Multilingual Sentence Encoder with Machine Translation</title>
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            <title>Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)</title>
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    <abstract>To reduce the cost of training models for each language for developers of natural language processing applications, pre-trained multilingual sentence encoders are promising.However, since training corpora for such multilingual sentence encoders contain only a small amount of text in languages other than English, they suffer from performance degradation for non-English languages.To improve the performance of pre-trained multilingual sentence encoders for non-English languages, we propose a method of machine translating a source sentence into English and then inputting it together with the source sentence in a multi-source manner.Experimental results on sentiment analysis and topic classification tasks in Japanese revealed the effectiveness of the proposed method.</abstract>
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%0 Conference Proceedings
%T Multi-Source Text Classification for Multilingual Sentence Encoder with Machine Translation
%A Kajikawa, Reon
%A Yamada, Keiichiro
%A Kajiwara, Tomoyuki
%A Ninomiya, Takashi
%Y Cao, Yang (Trista)
%Y Papadimitriou, Isabel
%Y Ovalle, Anaelia
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F kajikawa-etal-2024-multi
%X To reduce the cost of training models for each language for developers of natural language processing applications, pre-trained multilingual sentence encoders are promising.However, since training corpora for such multilingual sentence encoders contain only a small amount of text in languages other than English, they suffer from performance degradation for non-English languages.To improve the performance of pre-trained multilingual sentence encoders for non-English languages, we propose a method of machine translating a source sentence into English and then inputting it together with the source sentence in a multi-source manner.Experimental results on sentiment analysis and topic classification tasks in Japanese revealed the effectiveness of the proposed method.
%U https://aclanthology.org/2024.naacl-srw.24
%P 226-232

Markdown (Informal)

[Multi-Source Text Classification for Multilingual Sentence Encoder with Machine Translation](https://aclanthology.org/2024.naacl-srw.24) (Kajikawa et al., NAACL 2024)

• Multi-Source Text Classification for Multilingual Sentence Encoder with Machine Translation (Kajikawa et al., NAACL 2024)

ACL

• Reon Kajikawa, Keiichiro Yamada, Tomoyuki Kajiwara, and Takashi Ninomiya. 2024. Multi-Source Text Classification for Multilingual Sentence Encoder with Machine Translation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 226–232, Mexico City, Mexico. Association for Computational Linguistics.