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        <title>Towards a Unified End-to-End Approach for Fully Unsupervised Cross-Lingual Sentiment Analysis</title>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Sentiment analysis in low-resource languages suffers from the lack of training data. Cross-lingual sentiment analysis (CLSA) aims to improve the performance on these languages by leveraging annotated data from other languages. Recent studies have shown that CLSA can be performed in a fully unsupervised manner, without exploiting either target language supervision or cross-lingual supervision. However, these methods rely heavily on unsupervised cross-lingual word embeddings (CLWE), which has been shown to have serious drawbacks on distant language pairs (e.g. English - Japanese). In this paper, we propose an end-to-end CLSA model by leveraging unlabeled data in multiple languages and multiple domains and eliminate the need for unsupervised CLWE. Our model applies to two CLSA settings: the traditional cross-lingual in-domain setting and the more challenging cross-lingual cross-domain setting. We empirically evaluate our approach on the multilingual multi-domain Amazon review dataset. Experimental results show that our model outperforms the baselines by a large margin despite its minimal resource requirement.</abstract>
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%0 Conference Proceedings
%T Towards a Unified End-to-End Approach for Fully Unsupervised Cross-Lingual Sentiment Analysis
%A Feng, Yanlin
%A Wan, Xiaojun
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F feng-wan-2019-towards
%X Sentiment analysis in low-resource languages suffers from the lack of training data. Cross-lingual sentiment analysis (CLSA) aims to improve the performance on these languages by leveraging annotated data from other languages. Recent studies have shown that CLSA can be performed in a fully unsupervised manner, without exploiting either target language supervision or cross-lingual supervision. However, these methods rely heavily on unsupervised cross-lingual word embeddings (CLWE), which has been shown to have serious drawbacks on distant language pairs (e.g. English - Japanese). In this paper, we propose an end-to-end CLSA model by leveraging unlabeled data in multiple languages and multiple domains and eliminate the need for unsupervised CLWE. Our model applies to two CLSA settings: the traditional cross-lingual in-domain setting and the more challenging cross-lingual cross-domain setting. We empirically evaluate our approach on the multilingual multi-domain Amazon review dataset. Experimental results show that our model outperforms the baselines by a large margin despite its minimal resource requirement.
%R 10.18653/v1/K19-1097
%U https://aclanthology.org/K19-1097
%U https://doi.org/10.18653/v1/K19-1097
%P 1035-1044

Markdown (Informal)

[Towards a Unified End-to-End Approach for Fully Unsupervised Cross-Lingual Sentiment Analysis](https://aclanthology.org/K19-1097) (Feng & Wan, CoNLL 2019)

• Towards a Unified End-to-End Approach for Fully Unsupervised Cross-Lingual Sentiment Analysis (Feng & Wan, CoNLL 2019)

ACL

• Yanlin Feng and Xiaojun Wan. 2019. Towards a Unified End-to-End Approach for Fully Unsupervised Cross-Lingual Sentiment Analysis. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 1035–1044, Hong Kong, China. Association for Computational Linguistics.