Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?
Shaoxiong Ji, Timothee Mickus, Vincent Segonne, Jörg Tiedemann
Abstract
Multilingual pretraining and fine-tuning have remarkably succeeded in various natural language processing tasks. Transferring representations from one language to another is especially crucial for cross-lingual learning. One can expect machine translation objectives to be well suited to fostering such capabilities, as they involve the explicit alignment of semantically equivalent sentences from different languages. This paper investigates the potential benefits of employing machine translation as a continued training objective to enhance language representation learning, bridging multilingual pretraining and cross-lingual applications. We study this question through two lenses: a quantitative evaluation of the performance of existing models and an analysis of their latent representations. Our results show that, contrary to expectations, machine translation as the continued training fails to enhance cross-lingual representation learning in multiple cross-lingual natural language understanding tasks. We conclude that explicit sentence-level alignment in the cross-lingual scenario is detrimental to cross-lingual transfer pretraining, which has important implications for future cross-lingual transfer studies. We furthermore provide evidence through similarity measures and investigation of parameters that this lack of positive influence is due to output separability—which we argue is of use for machine translation but detrimental elsewhere.- Anthology ID:
- 2024.lrec-main.250
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 2809–2818
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.250
- DOI:
- Cite (ACL):
- Shaoxiong Ji, Timothee Mickus, Vincent Segonne, and Jörg Tiedemann. 2024. Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2809–2818, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning? (Ji et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.250.pdf