Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning
This work addresses word alignment for low-resource ASEAN languages, which is incremental as it builds on an existing BiLSTM framework with a novel contrastive learning component.
The paper tackled cross-lingual word alignment for low-resource ASEAN languages by incorporating contrastive learning into a BiLSTM-based encoder-decoder model, resulting in consistent accuracy improvements across five bilingual datasets.
Cross-lingual word alignment plays a crucial role in various natural language processing tasks, particularly for low-resource languages. Recent study proposes a BiLSTM-based encoder-decoder model that outperforms pre-trained language models in low-resource settings. However, their model only considers the similarity of word embedding spaces and does not explicitly model the differences between word embeddings. To address this limitation, we propose incorporating contrastive learning into the BiLSTM-based encoder-decoder framework. Our approach introduces a multi-view negative sampling strategy to learn the differences between word pairs in the shared cross-lingual embedding space. We evaluate our model on five bilingual aligned datasets spanning four ASEAN languages: Lao, Vietnamese, Thai, and Indonesian. Experimental results demonstrate that integrating contrastive learning consistently improves word alignment accuracy across all datasets, confirming the effectiveness of the proposed method in low-resource scenarios. We will release our data set and code to support future research on ASEAN or more low-resource word alignment.