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首页> 外文期刊>Audio, Speech, and Language Processing, IEEE/ACM Transactions on >Incorporating Statistical Machine Translation Word Knowledge Into Neural Machine Translation
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Incorporating Statistical Machine Translation Word Knowledge Into Neural Machine Translation

机译：将统计机器翻译单词知识整合到神经机器翻译中

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摘要

Neural machine translation (NMT) has gained more and more attention in recent years, mainly due to its simplicity yet state-of-the-art performance. However, previous research has shown that NMT suffers from several limitations: source coverage guidance, translation of rare words, and the limited vocabulary, while statistical machine translation (SMT) has complementary properties that correspond well to these limitations. It is straightforward to improve the translation performance by combining the advantages of two kinds of models. This paper proposes a general framework for incorporating the SMT word knowledge into NMT to alleviate above word-level limitations. In our framework, the NMT decoder makes more accurate word prediction by referring to the SMT word recommendations in both training and testing phases. Specifically, the SMT model offers informative word recommendations based on the NMT decoding information. Then, we use the SMT word predictions as prior knowledge to adjust the NMT word generation probability, which unitizes a neural network based classifier to digest the discrete word knowledge. In this paper, we use two model variants to implement the framework, one with a gating mechanism and the other with a direct competition mechanism. Experimental results on Chinese-to-English and English-to-German translation tasks show that the proposed framework can take advantage of the SMT word knowledge and consistently achieve significant improvements over NMT and SMT baseline systems.
机译：近年来，神经机器翻译（NMT）受到了越来越多的关注，这主要是由于其简单而先进的性能。但是，先前的研究表明NMT受到以下限制：来源覆盖指南，稀有单词的翻译和词汇量有限，而统计机器翻译（SMT）具有与这些限制很好对应的互补属性。通过结合两种模型的优点，可以直接提高翻译性能。本文提出了一种将SMT单词知识纳入NMT的通用框架，以减轻上述单词级别的限制。在我们的框架中，NMT解码器在训​​练和测试阶段都通过参考SMT单词建议来进行更准确的单词预测。具体而言，SMT模型基于NMT解码信息提供信息丰富的单词推荐。然后，我们使用SMT单词预测作为先验知识来调整NMT单词生成概率，这将基于神经网络的分类器统一起来以消化离散单词知识。在本文中，我们使用两种模型变体来实现该框架，一种具有门控机制，另一种具有直接竞争机制。在汉英翻译和英德翻译任务上的实验结果表明，所提出的框架可以利用SMT单词知识，并始终在NMT和SMT基准系统上取得重大改进。

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