We propose a framework for computerassisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal 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 marked words. The approach builds upon neural sequence-to-sequence modeling and introduces a neural network which takes as input a sentence along with change markers. Our model is trained on translation bitext by simulating post-edits. We demonstrate the advantage of our approach for translation postediting through simulated post-edits. We also evaluate our model for paraphrasing through a user study. Our work builds upon previous research on neural machine translation, machine translation postediting, and computer-assisted editing.
Our work draws inspiration from REF who post-edit the output of machine translation under the assumption that a human modifies a sentence by marking tokens they would like the system to change.
QuickEdit: Editing Text & Translations by Crossing Words Out
NAACL-HLT
Computer Science