Cross-Lingual Word Embeddings for Low-Resource Language Modeling
Oliver Adams, Adam Makarucha, Graham Neubig, Steven Bird, Trevor Cohn
Abstract
Most languages have no established writing system and minimal written records. However, textual data is essential for natural language processing, and particularly important for training language models to support speech recognition. Even in cases where text data is missing, there are some languages for which bilingual lexicons are available, since creating lexicons is a fundamental task of documentary linguistics. We investigate the use of such lexicons to improve language models when textual training data is limited to as few as a thousand sentences. The method involves learning cross-lingual word embeddings as a preliminary step in training monolingual language models. Results across a number of languages show that language models are improved by this pre-training. Application to Yongning Na, a threatened language, highlights challenges in deploying the approach in real low-resource environments.- Anthology ID:
- E17-1088
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 937–947
- Language:
- URL:
- https://aclanthology.org/E17-1088
- DOI:
- Cite (ACL):
- Oliver Adams, Adam Makarucha, Graham Neubig, Steven Bird, and Trevor Cohn. 2017. Cross-Lingual Word Embeddings for Low-Resource Language Modeling. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 937–947, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Word Embeddings for Low-Resource Language Modeling (Adams et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/E17-1088.pdf
- Data
- Panlex