Cross-lingual Transfer Performance of Multilingual vs. Monolingual Models in Low-Resource Languages

We address the task of machine translation (MT) from extremely low-resource language (ELRL) to English by leveraging cross-lingual transfer from 'closely-related' high-resource language (HRL). The development of an MT system for ELRL is challenging because these languages typically lack parallel corpora and monolingual corpora, and their representations are absent from large multilingual language models. Many ELRLs share lexical similarities with some HRLs, which presents a novel modeling opportunity. However, existing subword-based neural MT models do not explicitly harness this lexical simil

Research goal: How do multilingual models like XLM-R compare to monolingual models in zero-shot cross-lingual transfer for low-resource languages, and can lexical similarity metrics explain the observed differences in performance?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.