Existing approaches for unsupervised bilingual lexicon induction (BLI) often
depend on good quality static or contextual embeddings trained on large
monolingual corpora for both languages. In reality, however, unsupervised BLI
is most likely to be useful for dialects and languages that do not have
abundant amounts of monolingual data. We introduce a simple and fast method for
unsupervised BLI for low-resource languages with a related mid-to-high resource
language, only requiring inference on the higher-resource language monolingual
BERT. We work with two low-resource languages ($<5M$ monolingual tokens),
Bhojpuri and Magahi, of the severely under-researched Indic dialect continuum,
showing that state-of-the-art methods in the literature show near-zero
performance in these settings, and that our simpler method gives much better
results. We repeat our experiments on Marathi and Nepali, two higher-resource
Indic languages, to compare approach performances by resource range. We release
automatically created bilingual lexicons for the first time for five languages
of the Indic dialect continuum.

介绍了一种适用于资源匮乏语言的无监督双语词汇表感知方法，该方法仅需要使用高资源语言单语 BERT 进行推理，并在印度次大陆的五种语言中首次发布自动生成的双语词汇表。