论文标题
神经机器翻译的双重过去和未来
Dual Past and Future for Neural Machine Translation
论文作者
论文摘要
尽管近年来神经机器翻译（NMT）取得了显着的成功，但它仍然遭受了不足的翻译问题。先前的研究表明，对源句子的过去和未来进行明确建模对翻译性能有益。但是，尚不清楚常用的启发式目标是否足以指导过去和未来。在本文中，我们提出了一个新颖的双重框架，该框架利用源至目标和目标NMT模型为过去和将来的模块提供了更直接和准确的监督信号。实验结果表明，我们提出的方法显着提高了NMT预测的适当性，并超过了两个经过良好研究的翻译任务中的先前方法。
Though remarkable successes have been achieved by Neural Machine Translation (NMT) in recent years, it still suffers from the inadequate-translation problem. Previous studies show that explicitly modeling the Past and Future contents of the source sentence is beneficial for translation performance. However, it is not clear whether the commonly used heuristic objective is good enough to guide the Past and Future. In this paper, we present a novel dual framework that leverages both source-to-target and target-to-source NMT models to provide a more direct and accurate supervision signal for the Past and Future modules. Experimental results demonstrate that our proposed method significantly improves the adequacy of NMT predictions and surpasses previous methods in two well-studied translation tasks.
