论文标题
明确重新排序神经机器翻译
Explicit Reordering for Neural Machine Translation
论文作者
论文摘要
在基于变压器的神经机器翻译（NMT）中，位置编码机制有助于自我发挥的网络以订单依赖性学习源代表，这使得基于变压器的NMT实现了各种翻译任务的最新结果。但是，基于变压器的NMT仅依次添加位置的表示，并在输入句子中的单词向量添加了表示，并且不明确考虑在此句子中重新排序信息。在本文中，我们首先从经验上研究了源重新排序信息与翻译性能之间的关系。经验发现表明，从双语并行数据集中学到的目标顺序的源输入可以大大改善翻译性能。因此，我们提出了一种新型的重新排序方法，以明确地对基于变压器的NMT进行重新排序信息进行建模。 WMT14英语对德国人，WAT ASPEC日语对英语和WMT17中文对英语翻译任务的经验结果显示了拟议方法的有效性。
In Transformer-based neural machine translation (NMT), the positional encoding mechanism helps the self-attention networks to learn the source representation with order dependency, which makes the Transformer-based NMT achieve state-of-the-art results for various translation tasks. However, Transformer-based NMT only adds representations of positions sequentially to word vectors in the input sentence and does not explicitly consider reordering information in this sentence. In this paper, we first empirically investigate the relationship between source reordering information and translation performance. The empirical findings show that the source input with the target order learned from the bilingual parallel dataset can substantially improve translation performance. Thus, we propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT. The empirical results on the WMT14 English-to-German, WAT ASPEC Japanese-to-English, and WMT17 Chinese-to-English translation tasks show the effectiveness of the proposed approach.
