Data augmentation is an effective performance enhancement in neural machine
translation (NMT) by generating additional bilingual data. In this paper, we
propose a novel data augmentation enhancement strategy for neural machine
translation. Different from existing data augmentation methods which simply
choose words with the same probability across different sentences for
modification, we set sentence-specific probability for word selection by
considering their roles in sentence. We use dependency parse tree of input
sentence as an effective clue to determine selecting probability for every
words in each sentence. Our proposed method is evaluated on WMT14
English-to-German dataset and IWSLT14 German-to-English dataset. The result of
extensive experiments show our proposed syntax-aware data augmentation method
may effectively boost existing sentence-independent methods for significant
translation performance improvement.

本文提出了一种新的句法感知数据增强策略，使用输入句子的依存句法树来确定每个句子中单词的选择概率，以有效增强神经机器翻译的翻译性能。实验表明，所提出的数据增强方法可显著提高基于句子独立的方法的翻译性能。

神经机器翻译的句法感知数据增强

Syntax-aware Data Augmentation for Neural Machine Translation

In machine translation (MT) that involves translating between two languages
with significant differences in word order, determining the correct word order
of translated words is a major challenge. The dependency parse tree of a source
sentence can help to determine the correct word order of the translated words.
In this paper, we present a novel reordering approach utilizing a neural
network and dependency-based embeddings to predict whether the translations of
two source words linked by a dependency relation should remain in the same
order or should be swapped in the translated sentence. Experiments on
Chinese-to-English translation show that our approach yields a statistically
significant improvement of 0.57 BLEU point on benchmark NIST test sets,
compared to our prior state-of-the-art statistical MT system that uses sparse
dependency-based reordering features.

本文介绍了一种新的重排序方法，利用神经网络和基于依存性的嵌入来预测两个源单词之间的依存关系，以确定翻译的单词顺序，实现了在中英翻译中统计显著的 0.57 BLEU 分数的提高。

基于依赖关系的神经重排模型用于统计机器翻译

A Dependency-Based Neural Reordering Model for Statistical Machine  Translation

We describe an attentive encoder that combines tree-structured recursive
neural networks and sequential recurrent neural networks for modelling sentence
pairs. Since existing attentive models exert attention on the sequential
structure, we propose a way to incorporate attention into the tree topology.
Specially, given a pair of sentences, our attentive encoder uses the
representation of one sentence, which generated via an RNN, to guide the
structural encoding of the other sentence on the dependency parse tree. We
evaluate the proposed attentive encoder on three tasks: semantic similarity,
paraphrase identification and true-false question selection. Experimental
results show that our encoder outperforms all baselines and achieves
state-of-the-art results on two tasks.

本文介绍了一种关注编码器，结合了树状递归神经网络和序列循环神经网络，用于对模拟句子对。我们的关注编码器将 RNN 生成的一个句子的表示用于指导另一个句子的结构编码，评估结果表明，在语义相似度和释义选择方面，我们的编码器优于所有基线，并取得了最佳结果。