论文标题
学习使用模仿学习的同时机器翻译的耦合政策
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning
论文作者
论文摘要
我们提出了一种新颖的方法，可以有效地学习具有耦合程序员互换策略的同时翻译模型。首先，使用单词对齐的概念来训练双语句子对制作甲骨文读/写动作，以制作甲骨文读/写动作。此Oracle动作旨在在编写输出之前从部分输入中捕获足够的信息。接下来，我们执行一项耦合的计划抽样，以在共同学习两种政策与模仿学习时有效地减轻暴露偏见。六对语言的实验表明，我们的方法在翻译质量方面优于强大的基准，同时保持翻译延迟较低。
We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies. First, wepresent an algorithmic oracle to produce oracle READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments. This oracle actions are designed to capture enough information from the partial input before writing the output. Next, we perform a coupled scheduled sampling to effectively mitigate the exposure bias when learning both policies jointly with imitation learning. Experiments on six language-pairs show our method outperforms strong baselines in terms of translation quality while keeping the translation delay low.
