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.- Anthology ID:
- K19-1097
- Volume:
- Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Mohit Bansal, Aline Villavicencio
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1035–1044
- Language:
- URL:
- https://aclanthology.org/K19-1097
- DOI:
- 10.18653/v1/K19-1097
- Cite (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.
- Cite (Informal):
- Towards a Unified End-to-End Approach for Fully Unsupervised Cross-Lingual Sentiment Analysis (Feng & Wan, CoNLL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/K19-1097.pdf