State-of-the-art Transformer-based neural machine translation (NMT) systems
still follow a standard encoder-decoder framework, in which source sentence
representation can be well done by an encoder with self-attention mechanism.
Though Transformer-based encoder may effectively capture general information in
its resulting source sentence representation, the backbone information, which
stands for the gist of a sentence, is not specifically focused on. In this
paper, we propose an explicit sentence compression method to enhance the source
sentence representation for NMT. In practice, an explicit sentence compression
goal used to learn the backbone information in a sentence. We propose three
ways, including backbone source-side fusion, target-side fusion, and both-side
fusion, to integrate the compressed sentence into NMT. Our empirical tests on
the WMT English-to-French and English-to-German translation tasks show that the
proposed sentence compression method significantly improves the translation
performances over strong baselines.

该研究提出了一种明确的句子压缩方法来增强源句子对于 NMT 的表征，包括背景信息，提出三种整合压缩句子到 NMT 的方法，并在 WMT 英法和英德翻译任务上进行实验测试，结果显示该压缩方法显著提高了翻译表现。

神经机器翻译的显式句子压缩

Explicit Sentence Compression for Neural Machine Translation

Although attention-based Neural Machine Translation (NMT) has achieved
remarkable progress in recent years, it still suffers from issues of repeating
and dropping translations. To alleviate these issues, we propose a novel
key-value memory-augmented attention model for NMT, called KVMEMATT.
Specifically, we maintain a timely updated keymemory to keep track of attention
history and a fixed value-memory to store the representation of source sentence
throughout the whole translation process. Via nontrivial transformations and
iterative interactions between the two memories, the decoder focuses on more
appropriate source word(s) for predicting the next target word at each decoding
step, therefore can improve the adequacy of translations. Experimental results
on Chinese=>English and WMT17 German<=>English translation tasks demonstrate
the superiority of the proposed model.

提出了一种基于键值记忆的注意力机制模型用于神经机器翻译，通过维护及时更新的键内存来跟踪注意力历史和固定值内存来存储源语句的表示，在两个记忆之间进行非平凡的转换和迭代交互，以便每个解码步骤时，可以关注更合适的源单词来预测下一个目标单词从而提高翻译的适用性。在中英文和 WMT17 德英翻译任务的实验结果表明了所提出的模型的优越性。