论文标题
为强大的神经机器翻译建模谐波噪声
Modeling Homophone Noise for Robust Neural Machine Translation
论文作者
论文摘要
在本文中，我们提出了一个强大的神经机器翻译（NMT）框架。该框架由同型噪声检测器和与均音误差的音节感知的NMT模型组成。检测器在文本句子中识别潜在的同音错误，并将其转换为音节形成混合序列，然后将其馈入音节感知的NMT。关于中文 - >英语翻译的广泛实验表明，我们提出的方法不仅在具有同型噪声的嘈杂测试集上显着优于基准，而且在干净的文本方面也有了很大的改善。
In this paper, we propose a robust neural machine translation (NMT) framework. The framework consists of a homophone noise detector and a syllable-aware NMT model to homophone errors. The detector identifies potential homophone errors in a textual sentence and converts them into syllables to form a mixed sequence that is then fed into the syllable-aware NMT. Extensive experiments on Chinese->English translation demonstrate that our proposed method not only significantly outperforms baselines on noisy test sets with homophone noise, but also achieves a substantial improvement on clean text.
