Improving Cross-lingual Transfer with Contrastive Negative Learning and Self-training
Guanlin Li, Xuechen Zhao, Amir Jafari, Wenhao Shao, Reza Farahbakhsh, Noel Crespi
Abstract
Recent studies improve the cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. However, the alignment-based methods exhibit intrinsic limitations such as non-transferable linguistic elements, while most of the self-training based methods ignore the useful information hidden in the low-confidence samples. To address this issue, we propose CoNLST (Contrastive Negative Learning and Self-Training) to leverage the information of low-confidence samples. Specifically, we extend the negative learning to the metric space by selecting negative pairs based on the complementary labels and then employ self-training to iteratively train the model to converge on the obtained clean pseudo-labels. We evaluate our approach on the widely-adopted cross-lingual benchmark XNLI. The experiment results show that our method improves upon the baseline models and can serve as a beneficial complement to the alignment-based methods.- Anthology ID:
- 2024.lrec-main.769
- 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:
- 8781–8791
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.769
- DOI:
- Cite (ACL):
- Guanlin Li, Xuechen Zhao, Amir Jafari, Wenhao Shao, Reza Farahbakhsh, and Noel Crespi. 2024. Improving Cross-lingual Transfer with Contrastive Negative Learning and Self-training. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8781–8791, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Improving Cross-lingual Transfer with Contrastive Negative Learning and Self-training (Li et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.769.pdf