Explicit Sentence Compression for Neural Machine Translation
This addresses a specific bottleneck in neural machine translation for translation system developers, though it appears incremental rather than paradigm-shifting.
The paper tackles the problem that Transformer-based NMT systems don't specifically focus on the backbone information (gist) of source sentences, proposing an explicit sentence compression method to enhance source representation. The method improves translation performance on WMT English-to-French and English-to-German tasks over strong baselines.
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.