Even though a linguistics-free sequence to sequence model in neural machine
translation (NMT) has certain capability of implicitly learning syntactic
information of source sentences, this paper shows that source syntax can be
explicitly incorporated into NMT effectively to provide further improvements.
Specifically, we linearize parse trees of source sentences to obtain structural
label sequences. On the basis, we propose three different sorts of encoders to
incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word
and label annotation vectors parallelly; 2) Hierarchical RNN encoder that
learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed
RNN encoder that stitchingly learns word and label annotation vectors over
sequences where words and labels are mixed. Experimentation on
Chinese-to-English translation demonstrates that all the three proposed
syntactic encoders are able to improve translation accuracy. It is interesting
to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best
performance with an significant improvement of 1.4 BLEU points. Moreover, an
in-depth analysis from several perspectives is provided to reveal how source
syntax benefits NMT.

本文介绍了三种不同类型的编码器，用于将源句法明确地合并到神经机器翻译 (NMT) 中，并在中英文翻译实验中证明了这三种编码器的有效性，尤其是 Mixed RNN encoder 能有效提升 1.4 BLEU points，同时提供了深入分析来揭示源语法如何有益于 NMT。

神经机器翻译中的源语法建模

Modeling Source Syntax for Neural Machine Translation

In this paper, we propose phraseNet, a neural machine translator with a
phrase memory which stores phrase pairs in symbolic form, mined from corpus or
specified by human experts. For any given source sentence, phraseNet scans the
phrase memory to determine the candidate phrase pairs and integrates tagging
information in the representation of source sentence accordingly. The decoder
utilizes a mixture of word-generating component and phrase-generating
component, with a specifically designed strategy to generate a sequence of
multiple words all at once. The phraseNet not only approaches one step towards
incorporating external knowledge into neural machine translation, but also
makes an effort to extend the word-by-word generation mechanism of recurrent
neural network. Our empirical study on Chinese-to-English translation shows
that, with carefully-chosen phrase table in memory, phraseNet yields 3.45 BLEU
improvement over the generic neural machine translator.

利用短语记忆，结合外部知识，采用生成多个词语序列的策略，提出一种神经机器翻译器 PhraseNet。在汉译英实验中，PhraseNet 相比普通神经机器翻译器，BLEU 值提高了 3.45。