Low-Resource MT
MT: Machine Translation
Overview
The field studies how to translate languages that lack the large parallel-text collections and language technologies available for widely used languages, helping broaden access to information and services. Common approaches include building curated corpora, adapting multilingual models such as NLLB and mT5, transferring knowledge from related languages, and creating synthetic training data through back-translation or language-model generation. Researchers also explore retrieval-based prompting, linguistic resources such as dictionaries and grammars, and reinforcement learning with semantic or quality-estimation rewards. Progress remains uneven: results depend heavily on data quality, language variety, script and tokenization, while metric reliability, data contamination, and the gap between automatic scores and human judgment remain open concerns.
Latest papers
87 papers15 papers in the last four weeks, up 67% on the four weeks before. 0.1% of all new papers.