Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!

While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural "shortcuts", such as copying subwords from the source to the target, given that such language pairs often share a considerable number of identical words, cognates, and borrowings. We test Pointer-Generator Networks for this purpose for six language pairs over a variety of resource ranges, and find weak improvements for most settings. However, analysis shows that the model does not show greater improvements for closely-related vs. more distant language pairs, or for lower resource ranges, and that the models do not exhibit the expected usage of the mechanism for shared subwords. Our discussion of the reasons for this behaviour highlights several general challenges for LR NMT, such as modern tokenization strategies, noisy real-world conditions, and linguistic complexities. We call for better scrutiny of linguistically motivated improvements to NMT given the blackbox nature of Transformer models, as well as for a focus on the above problems in the field.

A Simple Method for Unsupervised Bilingual Lexicon Induction for Data-Imbalanced, Closely Related Language Pairs

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.