Unified End-to-End Sentence Denoising

It takes more than correct grammar to speak good English. In this paper, we describe the sentence denoising task that reduces the vagueness, redundancy, and irrationality of a grammatical sentence. We define a rich, linguistics-inspired noise taxonomy and establish the formal definition of the problem. A unified end-to-end model based on Transformer is proposed and an efficient algorithm for constructing the training data is given, together with a separate fine-tuning step to get the ideal model. Our method outperforms previous results and keeps good accuracy as the noise composition gets more complicated.