Abstract
Recent progress on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embedding space for several languages. A popular framework to solve the latter problem is to solve the following two sub-problems jointly: 1) learning unsupervised word alignment between several language pairs, and 2) learning how to map the monolingual embeddings of every language to shared multilingual space. In contrast, we propose a simple approach by decoupling the above two sub-problems and solving them separately, one after another, using existing techniques. We show that this proposed approach obtains surprisingly good performance in tasks such as bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing. When distant languages are involved, the proposed approach shows robust behavior and outperforms existing unsupervised multilingual word embedding approaches.- Anthology ID:
- 2020.emnlp-main.240
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2995–3001
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.240
- DOI:
- 10.18653/v1/2020.emnlp-main.240
- Cite (ACL):
- Pratik Jawanpuria, Mayank Meghwanshi, and Bamdev Mishra. 2020. A Simple Approach to Learning Unsupervised Multilingual Embeddings. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2995–3001, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Simple Approach to Learning Unsupervised Multilingual Embeddings (Jawanpuria et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.240.pdf