Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction

Yuta Hitomi, Hideaki Tamori, Naoaki Okazaki, Kentaro Inui

Abstract: This paper explores the idea of robot editors, automated proofreaders that enable journalists to improve the quality of their articles. We propose a novel neural model of multi-task learning that both generates proofread sentences and predicts the editing operations required to rewrite the source sentences and create the proofread ones. The model is trained using logs of the revisions made professional editors revising draft newspaper articles written by journalists. Experiments demonstrate the effectiveness of our multi-task learning approach and the potential value of using revision logs for this task.