论文标题
源语法的编码：跨目标语言中NMT表示的相似性
Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages
论文作者
论文摘要
我们使用NMT编码器表示来预测源语言单词的祖先组成标签，从而将神经机器翻译（NMT）模型从英语训练从英语到六个目标语言。我们发现，NMT编码者学习类似的源语法，无论NMT目标语言如何，都依靠明确的形态句法提示从源句子中提取句法特征。此外，NMT编码直接在几个组成标签预测任务上训练的较高训练，这表明NMT编码器表示可以有效地用于涉及语法的自然语言任务。但是，NMT编码器和直接训练的RNN都从无概率的无上下文语法（PCFG）解析器中学习了根本不同的句法信息。尽管总体精度得分较低，但PCFG通常在基于RNN的模型效果较差的句子上表现良好，这表明RNN体系结构在他们可以学习的语法类型中受到限制。
We train neural machine translation (NMT) models from English to six target languages, using NMT encoder representations to predict ancestor constituent labels of source language words. We find that NMT encoders learn similar source syntax regardless of NMT target language, relying on explicit morphosyntactic cues to extract syntactic features from source sentences. Furthermore, the NMT encoders outperform RNNs trained directly on several of the constituent label prediction tasks, suggesting that NMT encoder representations can be used effectively for natural language tasks involving syntax. However, both the NMT encoders and the directly-trained RNNs learn substantially different syntactic information from a probabilistic context-free grammar (PCFG) parser. Despite lower overall accuracy scores, the PCFG often performs well on sentences for which the RNN-based models perform poorly, suggesting that RNN architectures are constrained in the types of syntax they can learn.
