Revisiting Machine Translation for Cross-lingual Classification
Mikel Artetxe, Vedanuj Goswami, Shruti Bhosale, Angela Pei-Chen Fan, Luke Zettlemoyer · 2023
Machine Translation (MT) has been widely used for cross-lingual classification, either by translating the test set into English and running inference with a monolingual model (translatetest), or translating the training set into the target languages and finetuning a multilingual model (translate-train).However, most research in the area focuses on the multilingual models rather than the MT component.We show that, by using a stronger MT system and mitigating the mismatch between training on original text and running inference on machine translated text, translate-test can do substantially better than previously assumed.The optimal approach, however, is highly task dependent, as we identify various sources of cross-lingual transfer gap that affect different tasks and approaches differently.Our work calls into question the dominance of multilingual models for cross-lingual classification, and prompts to pay more attention to MTbased baselines.