Revisiting Machine Translation for Cross-lingual Classification
Abstract
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.
Cite
@article{arxiv.2305.14240,
title = {Revisiting Machine Translation for Cross-lingual Classification},
author = {Mikel Artetxe and Vedanuj Goswami and Shruti Bhosale and Angela Fan and Luke Zettlemoyer},
journal= {arXiv preprint arXiv:2305.14240},
year = {2023}
}