QuickEdit: Editing text & translations by crossing words out
D Grangier, M Auli - arXiv preprint arXiv:1711.04805, 2017 - arxiv.org
arXiv preprint arXiv:1711.04805, 2017•arxiv.org
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 …
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 …
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.
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