  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.
