Collecting parallel sentences from nonparallel data is a long-standing natural language processing research problem. In particular, parallel training sentences are very important for the quality of machine translation systems. While many existing methods have shown encouraging results, they cannot learn various alignment weights in parallel sentences. To address this issue, we propose a novel parallel hierarchical attention neural network which encodes monolingual sentences versus bilingual sentences and construct a classifier to extract parallel sentences. In particular, our attention mechanism structure can learn different alignment weights of words in parallel sentences. Experimental results show that our model can obtain state-of-the-art performance on the English-French, English-German, and English-Chinese dataset of BUCC 2017 shared task about parallel sentences’ extraction.

中文翻译：

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使用并行层次注意网络从非并行语料库中提取并行句子。

从非并行数据中收集并行句子是一个长期存在的自然语言处理研究问题。特别地，并行训练句子对于机器翻译系统的质量非常重要。尽管许多现有方法显示出令人鼓舞的结果，但它们无法在并行句子中学习各种对齐权重。为了解决这个问题，我们提出了一种新颖的并行层次注意神经网络，该网络对单语句子和双语句子进行编码，并构造一个分类器来提取平行句子。特别是，我们的注意力机制结构可以学习平行句子中单词的不同对齐权重。实验结果表明，我们的模型可以在英语-法语，英语-德语，