论文标题
邻居不是陌生人：在低频词汇约束下改善非自动回忆的翻译
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints
论文作者
论文摘要
但是，当前的自回旋方法遭受了高潜伏期的影响。在本文中，我们针对此问题的效率优势着重于非自动回旋翻译（NAT）。我们确定基于迭代编辑的当前约束NAT模型不能很好地处理低频约束。为此，我们为此工作提出了一种插入式算法，即，对齐受约束的培训（ACT），通过将模型熟悉约束的源端上下文来减轻此问题。一般和域数据集的实验表明，我们的模型在约束保存和翻译质量方面对主链约束NAT模型进行了改进，尤其是对于稀有约束。
However, current autoregressive approaches suffer from high latency. In this paper, we focus on non-autoregressive translation (NAT) for this problem for its efficiency advantage. We identify that current constrained NAT models, which are based on iterative editing, do not handle low-frequency constraints well. To this end, we propose a plug-in algorithm for this line of work, i.e., Aligned Constrained Training (ACT), which alleviates this problem by familiarizing the model with the source-side context of the constraints. Experiments on the general and domain datasets show that our model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints.
