1. Paper title

Content Word Aware Neural Machine Translation

2. link

https://www.aclweb.org/anthology/2020.acl-main.34.pdf

3. 摘要

Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-byword. However, NMT does not consider the importance of word in the sentence meaning, for example, some words (i.e., content words) express more important meaning than others (i.e., function words). To address this limitation, we first utilize word frequency information to distinguish between content and function words in a sentence, and then design a content word-aware NMT to improve translation performance. Empirical results on the WMT14 English-to-German, WMT14 English-to-French, and WMT17 Chineseto-English translation tasks show that the proposed methods can significantly improve the performance of Transformer-based NMT.

4. 要解决什么问题

NMT缺少从重要单词中抽取信息的的机制。

5. 作者的主要贡献

（1）使用单词频度信息把原文中的单词分为content和function两类。
（2）把content单词序列用于翻译1.把content单词序列当作是额外的信息向量化2.针对content单词序列设计特殊的loss

6. 得到了什么结果

在以下数据集上有明显的性能提升。
WMT14 English-to-German, WMT14 English-to-French, and WMT17 Chineseto-English

7. 关键字

预处理