论文标题
准确的在线后对齐，用于有原则的词汇约束解码
Accurate Online Posterior Alignments for Principled Lexically-Constrained Decoding
论文作者
论文摘要
机器翻译中的在线对齐是指在仅部分解码目标序列时将目标单词与源单词对齐的任务。良好的在线对齐促进了重要的应用程序，例如使用用户定义的词典将词汇约束注入翻译模型的重要应用。我们提出了一种新型的后验一致技术，该技术在其执行中真正在线上是真正的，并且与现有方法相比，对齐错误率很高。我们提出的推理技术以原则性的方式共同考虑对齐和令牌概率，并且可以无缝集成在现有的约束梁搜索解码算法中。在五对语言对中，包括两个遥远的语言对，我们达到了一致的对齐错误率。当部署在七个词汇约束的翻译任务上时，我们特别在受约束位置方面实现了BLEU的重大改进。
Online alignment in machine translation refers to the task of aligning a target word to a source word when the target sequence has only been partially decoded. Good online alignments facilitate important applications such as lexically constrained translation where user-defined dictionaries are used to inject lexical constraints into the translation model. We propose a novel posterior alignment technique that is truly online in its execution and superior in terms of alignment error rates compared to existing methods. Our proposed inference technique jointly considers alignment and token probabilities in a principled manner and can be seamlessly integrated within existing constrained beam-search decoding algorithms. On five language pairs, including two distant language pairs, we achieve consistent drop in alignment error rates. When deployed on seven lexically constrained translation tasks, we achieve significant improvements in BLEU specifically around the constrained positions.
