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        <title>Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision</title>
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    <abstract>Word embeddings learned in two languages can be mapped to a common space to produce Bilingual Word Embeddings (BWE). Unsupervised BWE methods learn such a mapping without any parallel data. However, these methods are mainly evaluated on tasks of word translation or word similarity. We show that these methods fail to capture the sentiment information and do not perform well enough on cross-lingual sentiment analysis. In this work, we propose UBiSE (Unsupervised Bilingual Sentiment Embeddings), which learns sentiment-specific word representations for two languages in a common space without any cross-lingual supervision. Our method only requires a sentiment corpus in the source language and pretrained monolingual word embeddings of both languages. We evaluate our method on three language pairs for cross-lingual sentiment analysis. Experimental results show that our method outperforms previous unsupervised BWE methods and even supervised BWE methods. Our method succeeds for a distant language pair English-Basque.</abstract>
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%0 Conference Proceedings
%T Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision
%A Feng, Yanlin
%A Wan, Xiaojun
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F feng-wan-2019-learning
%X Word embeddings learned in two languages can be mapped to a common space to produce Bilingual Word Embeddings (BWE). Unsupervised BWE methods learn such a mapping without any parallel data. However, these methods are mainly evaluated on tasks of word translation or word similarity. We show that these methods fail to capture the sentiment information and do not perform well enough on cross-lingual sentiment analysis. In this work, we propose UBiSE (Unsupervised Bilingual Sentiment Embeddings), which learns sentiment-specific word representations for two languages in a common space without any cross-lingual supervision. Our method only requires a sentiment corpus in the source language and pretrained monolingual word embeddings of both languages. We evaluate our method on three language pairs for cross-lingual sentiment analysis. Experimental results show that our method outperforms previous unsupervised BWE methods and even supervised BWE methods. Our method succeeds for a distant language pair English-Basque.
%R 10.18653/v1/N19-1040
%U https://aclanthology.org/N19-1040
%U https://doi.org/10.18653/v1/N19-1040
%P 420-429

Markdown (Informal)

[Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision](https://aclanthology.org/N19-1040) (Feng & Wan, NAACL 2019)

• Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision (Feng & Wan, NAACL 2019)

ACL

• Yanlin Feng and Xiaojun Wan. 2019. Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 420–429, Minneapolis, Minnesota. Association for Computational Linguistics.