Explicit Sentence Compression for Neural Machine Translation

Understand

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.