论文标题
双向神经机器翻译：双向翻译建模的概念证明使用二维网格
Two-Way Neural Machine Translation: A Proof of Concept for Bidirectional Translation Modeling using a Two-Dimensional Grid
论文作者
论文摘要
事实证明，神经翻译模型可以有效地从源句子中捕获足够的信息并产生高质量的目标句子。但是，对于双向翻译，即使用单个模型的双向翻译，即源对目标和目标转换都不容易。如果我们排除了一些开拓性尝试，例如多语言系统，则需要所有其他双向翻译方法来培训两个单独的模型。本文建议使用二维网格构建单个端到端的双向翻译模型，其中左右解码会生成源对目标，而底部解码会创建目标对源输出。我们的方法没有独立训练两个模型，而是鼓励单个网络共同学习向两个方向翻译。 WMT 2018德国$ \ leftrightArrow $英语和土耳其$ \ leftrightarrow $英语翻译任务的实验表明，拟议的模型能够产生良好的翻译质量，并且具有指导研究的足够潜力。
Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence. However, it is not easy to get the best effect for bidirectional translation, i.e., both source-to-target and target-to-source translation using a single model. If we exclude some pioneering attempts, such as multilingual systems, all other bidirectional translation approaches are required to train two individual models. This paper proposes to build a single end-to-end bidirectional translation model using a two-dimensional grid, where the left-to-right decoding generates source-to-target, and the bottom-to-up decoding creates target-to-source output. Instead of training two models independently, our approach encourages a single network to jointly learn to translate in both directions. Experiments on the WMT 2018 German$\leftrightarrow$English and Turkish$\leftrightarrow$English translation tasks show that the proposed model is capable of generating a good translation quality and has sufficient potential to direct the research.
