QuickEdit: Editing Text & Translations via Simple Delete Actions

We propose a framework for computerassisted text editing. It applies to translation post-editing and to paraphrasing and relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change. Our model then generates a new sentence which reformulates the initial sentence by avoiding the words from the marked tokens. Our approach builds upon neural sequence-to-sequence modeling and introduces a neural network which takes as input a sentence along with deleted token markers. Our model is trained on translation bitext by simulating post-edits. Our results on post-editing for machine translation and paraphrasing evaluate the performance of our approach. We show +11.4 BLEU with limited post-editing effort on the WMT-14 EnglishGerman translation task (25.2→ 36.6), which represents +5.9 BLEU over the post-editing baseline (30.7→ 36.6).