Projects per year
Abstract
Automated Post-Editing (PE) is the task of automatically correcting common and repetitive errors found in machine translation (MT) output. In this paper, we present a neural programmer-interpreter approach to this task, resembling the way that humans perform post-editing using discrete edit operations, which we refer to as programs. Our model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and - 0.7 TER scores.
Original language | English |
---|---|
Title of host publication | EMNLP 2018 |
Subtitle of host publication | Brussels, Belgium Oct. 31-Nov. 4 |
Editors | David Chiang, Julia Hockenmaier, Jun'ichi Tsujii |
Place of Publication | Stroudsburg PA USA |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 3048-3053 |
Number of pages | 6 |
ISBN (Electronic) | 9781948087841 |
Publication status | Published - 2018 |
Event | Empirical Methods in Natural Language Processing 2018 - Brussels, Belgium Duration: 31 Oct 2018 → 4 Nov 2018 https://emnlp2018.org/ https://www.aclweb.org/anthology/volumes/D18-1/ (Proceedings) |
Conference
Conference | Empirical Methods in Natural Language Processing 2018 |
---|---|
Abbreviated title | EMNLP 2018 |
Country/Territory | Belgium |
City | Brussels |
Period | 31/10/18 → 4/11/18 |
Internet address |
Projects
- 1 Finished
-
Learning Deep Semantics for Automatic Translation between Human Languages
Haffari, R., Cohn, T. & Blunsom, P.
Australian Research Council (ARC)
1/01/16 → 31/12/18
Project: Research