Abstract
Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. Such transfer emerges by fine-tuning on a task of interest in one language and evaluating on a distinct language, not seen during the fine-tuning. Despite promising results, we still lack a proper understanding of the source of this transfer. Using a novel layer ablation technique and analyses of the model’s internal representations, we show that multilingual BERT, a popular multilingual language model, can be viewed as the stacking of two sub-networks: a multilingual encoder followed by a task-specific language-agnostic predictor. While the encoder is crucial for cross-lingual transfer and remains mostly unchanged during fine-tuning, the task predictor has little importance on the transfer and can be reinitialized during fine-tuning. We present extensive experiments with three distinct tasks, seventeen typologically diverse languages and multiple domains to support our hypothesis.- Anthology ID:
- 2021.eacl-main.189
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2214–2231
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.189
- DOI:
- 10.18653/v1/2021.eacl-main.189
- Cite (ACL):
- Benjamin Muller, Yanai Elazar, Benoît Sagot, and Djamé Seddah. 2021. First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2214–2231, Online. Association for Computational Linguistics.
- Cite (Informal):
- First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT (Muller et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.eacl-main.189.pdf
- Code
- benjamin-mlr/first-align-then-predict