利用图注意网络将AMR集成到神经机器翻译中

Long H. B. Nguyen, Viet H. Pham, D. Dinh
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引用次数: 2

摘要

语义表示对于加强语义保存和提高机器翻译方法的泛化性能具有潜在的作用。本文将抽象意义表示(AMR)语义图中的语义信息整合到神经机器翻译中。首先,我们使用图注意网络(GATs)将AMR图编码为向量空间。然后,我们提出了一种有效的方法将语义表示整合到注意-编码器-解码器翻译模型中。实验结果表明,英语-越南语对的BLEU分数比基线法有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating AMR to Neural Machine Translation using Graph Attention Networks
Semantic representation is potentially useful to enforce meaning preservation and improve generalization performance of machine translation methods. In this paper, we incorporate semantic information from Abstract Meaning Representation (AMR) semantic graphs into neural machine translation. First, we use Graph Attention Networks (GATs) to encode the AMR graphs into a vector space. Then, we propose an effective way to integrate the semantic representation to the attention-encoder-decoder translation model. The experimental results show the improvements in BLEU scores over the baseline method on the English-Vietnamese language pair.
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