基于对比学习的东盟语言跨语言词对齐
Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning
AI总结：
针对低资源东盟语言词对齐，本文在基于 BiLSTM 的编码器-解码器框架中引入对比学习与多视角负采样，显式建模词嵌入差异，并在四个语种、五个数据集上验证其持续提升对齐准确率。
摘要：
跨语言词对齐在各类自然语言处理任务中发挥着关键作用，尤其对于低资源语言而言。近期研究提出了一种基于 BiLSTM 的编码器-解码器模型，在低资源场景下表现优于预训练语言模型。然而，该模型仅考虑词嵌入空间的相似性，并未显式建模词嵌入之间的差异。为解决这一局限，我们提出将对比学习融入基于 BiLSTM 的编码器-解码器框架。该方法引入多视角负采样策略，以学习共享跨语言嵌入空间中词对之间的差异。我们在涵盖老挝语、越南语、泰语和印度尼西亚语四种东盟语言的五个双语句对齐数据集上评估了模型。实验结果表明，融入对比学习能够在所有数据集上持续提升词对齐准确率，证实了所提方法在低资源场景中的有效性。我们将发布数据集和代码，以支持未来关于东盟语言或更多低资源语言词对齐的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
