Revisiting Machine Translation for Cross-lingual Classification
Mikel Artetxe, Vedanuj Goswami, Shruti Bhosale, Angela Fan, Luke Zettlemoyer
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.- Anthology ID:
- 2023.emnlp-main.399
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6489–6499
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.399
- DOI:
- 10.18653/v1/2023.emnlp-main.399
- Cite (ACL):
- Mikel Artetxe, Vedanuj Goswami, Shruti Bhosale, Angela Fan, and Luke Zettlemoyer. 2023. Revisiting Machine Translation for Cross-lingual Classification. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6489–6499, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Revisiting Machine Translation for Cross-lingual Classification (Artetxe et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.emnlp-main.399.pdf