Abstract
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.- Anthology ID:
- N18-1025
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 272–282
- Language:
- URL:
- https://aclanthology.org/N18-1025
- DOI:
- 10.18653/v1/N18-1025
- Cite (ACL):
- David Grangier and Michael Auli. 2018. QuickEdit: Editing Text & Translations by Crossing Words Out. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 272–282, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- QuickEdit: Editing Text & Translations by Crossing Words Out (Grangier & Auli, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/N18-1025.pdf
- Data
- WMT 2014