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Multi-Level Contrastive Learning for Cross-Lingual Alignment

2022-02-26 07:14:20
Beiduo Chen, Wu Guo, Bin Gu, Quan Liu, Yongchao Wang

Abstract

Cross-language pre-trained models such as multilingual BERT (mBERT) have achieved significant performance in various cross-lingual downstream NLP tasks. This paper proposes a multi-level contrastive learning (ML-CTL) framework to further improve the cross-lingual ability of pre-trained models. The proposed method uses translated parallel data to encourage the model to generate similar semantic embeddings for different languages. However, unlike the sentence-level alignment used in most previous studies, in this paper, we explicitly integrate the word-level information of each pair of parallel sentences into contrastive learning. Moreover, cross-zero noise contrastive estimation (CZ-NCE) loss is proposed to alleviate the impact of the floating-point error in the training process with a small batch size. The proposed method significantly improves the cross-lingual transfer ability of our basic model (mBERT) and outperforms on multiple zero-shot cross-lingual downstream tasks compared to the same-size models in the Xtreme benchmark.

Abstract (translated)

URL

https://arxiv.org/abs/2202.13083

PDF

https://arxiv.org/pdf/2202.13083.pdf


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