Multilingual Coreference Resolution via Cycle-Consistent Machine Translation
Researchers propose a novel coreference resolution pipeline that uses machine translation and cycle-consistency validation to improve NLP performance in low-resource languages. By translating English training data to target languages and back-translating to verify quality, the approach generates weighted training samples that significantly enhance coreference resolution accuracy, even enabling resolution in languages without existing corpora.