Abstract
Although neural machine translation (NMT) with the encoder-decoder framework has achieved great success in recent times, it still suffers from some drawbacks: RNNs tend to forget old information which is often useful in the current step and the encoder only operates over words without considering word relationship. To solve these problems, we introduce relation networks (RNs) to learn better representations of the source. In our method RNs are used to associate source words with each other so that the source representation can memorize all the source words and also contain the relationship between them. Then the source representations and all the relations are fed into the attention component together while decoding, with the main encoder-decoder architecture unchanged. Experiments on several data sets show that our method can improve the translation performance significantly over the conventional encoder-decoder model, and can even outperform the approach involving supervised syntactic knowledge.
Abstract (translated)
虽然编码器 - 解码器框架的神经机器翻译(NMT)近来取得了巨大的成功,但它仍然存在一些缺点:RNN倾向于忘记在当前步骤中通常有用的旧信息,并且编码器仅操作字考虑单词关系。为了解决这些问题,我们引入了关系网络(RN)来学习更好的源代表。在我们的方法中,RN用于将源词相互关联,以便源表示可以记住所有源词并且也包含它们之间的关系。然后将源表示和所有关系一起馈送到关注组件中,同时解码,主编码器 - 解码器架构不变。对几个数据集的实验表明,我们的方法可以比传统的编码器 - 解码器模型显着提高翻译性能,甚至可以超越涉及监督句法知识的方法。
URL
https://arxiv.org/abs/1805.11154