Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment

TL;DR

This paper introduces an unsupervised pipeline combining bitext mining and word alignment to significantly improve bilingual lexicon induction, outperforming previous methods and enabling more interpretable language translation models.

Contribution

It presents a novel unsupervised approach that integrates bitext construction and word alignment, achieving state-of-the-art results in bilingual lexicon induction.

Findings

Outperforms state-of-the-art on BUCC 2020 shared task by 14 F1 points

Produces lexicons of comparable quality to standard references

Suggests new benchmarks are needed for future progress

Abstract

Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces. In this paper, we show it is possible to produce much higher quality lexicons with methods that combine (1) unsupervised bitext mining and (2) unsupervised word alignment. Directly applying a pipeline that uses recent algorithms for both subproblems significantly improves induced lexicon quality and further gains are possible by learning to filter the resulting lexical entries, with both unsupervised and semi-supervised schemes. Our final model outperforms the state of the art on the BUCC 2020 shared task by 14 $F_1$ points averaged over 12 language pairs, while also providing a more interpretable approach that allows for rich reasoning of word meaning in context. Further analysis of our output and the standard reference lexicons suggests they are of comparable quality, and new benchmarks may be needed to measure further progress on this task.