Comparison of WEAM Alignment Performance to Adversarial and Contrastive Methods on XNLI via Zero-Shot F1 Scores

Abstract

Abstract: Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning. Some multilingual models such as mBERT, have been pre-trained on unlabeled corpora, therefore the embeddings of different languages in the models may not be aligned very well. In this paper, we aim to improve the zero-shot cross-lingual transfer performance by proposing a pre-training task named Word-Exchange Aligning Model (WEAM), which uses the statistical alignment information as the prior knowledge to guide cross-lingual word prediction. We evaluate our model on multilingual machine rea

Research Question

How does the alignment performance of WEAM compare to other alignment methods (e.g., adversarial alignment, contrastive learning) on XNLI when measured by F1 score in zero-shot cross-lingual transfer?

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