论文标题
对抗性神经机器翻译的对抗子词正则
Adversarial Subword Regularization for Robust Neural Machine Translation
论文作者
论文摘要
将各种子词分段暴露于神经机器翻译（NMT）模型通常会改善机器翻译的稳健性，因为NMT模型可以体验各种子字候选。但是，子单词分割的多元化主要依赖于预先训练的子词语言模型，从中，从中不可见的单词的错误分割不太可能被采样。在本文中，我们介绍了对抗性子词正则化（ADVSR），以研究训练期间的梯度信号是否可以成为暴露多种子词细分的替代标准。我们从实验上表明，我们的基于模型的对抗样本有效地鼓励NMT模型对分割误差的敏感性较低，并改善低资源和外域数据集中NMT模型的性能。
Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword candidates. However, the diversification of subword segmentations mostly relies on the pre-trained subword language models from which erroneous segmentations of unseen words are less likely to be sampled. In this paper, we present adversarial subword regularization (ADVSR) to study whether gradient signals during training can be a substitute criterion for exposing diverse subword segmentations. We experimentally show that our model-based adversarial samples effectively encourage NMT models to be less sensitive to segmentation errors and improve the performance of NMT models in low-resource and out-domain datasets.
