Revisiting Machine Translation for Cross-lingual Classification
Mikel Artetxe Vedanuj Goswami Shruti Bhosale Angela Fan Luke Zettlemoyer
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 (translate-test), 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 MT-based baselines.