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
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
937–947
Language:
URL:
https://www.aclweb.org/anthology/E17-1088
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-1088.pdf