QuickEdit: Editing Text & Translations by Crossing Words Out
This work addresses text editing for human editors, but it is incremental as it builds on existing neural sequence-to-sequence methods with a focus on specific applications.
The authors tackled the problem of computer-assisted text editing for translation post-editing and paraphrasing by proposing a framework where users mark tokens to change, and the model generates new sentences avoiding those words, demonstrating advantages through simulated post-edits and a user study.
We propose a framework for computer-assisted 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 post-editing through simulated post-edits. We also evaluate our model for paraphrasing through a user study.