Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment

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.

arXiv commentACL-IJCNLP 2021 camera-ready version, with full supplementary material

References in corpus (5)

• Exploiting Similarities among Languages for Machine Translation
• Offline bilingual word vectors, orthogonal transformations and the inverted softmax
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• An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification
• Bilingual Lexicon Induction through Unsupervised Machine Translation