Abstract
This paper presents the Automatic Post-editing (APE) systems submitted by the DFKI-MLT group to the WMT’18 APE shared task. Three monolingual neural sequence-to-sequence APE systems were trained using target-language data only: one using an attentional recurrent neural network architecture and two using the attention-only (transformer) architecture. The training data was composed of machine translated (MT) output used as source to the APE model aligned with their manually post-edited version or reference translation as target. We made use of the provided training sets only and trained APE models applicable to phrase-based and neural MT outputs. Results show better performances reached by the attention-only model over the recurrent one, significant improvement over the baseline when post-editing phrase-based MT output but degradation when applied to neural MT output.- Anthology ID:
- W18-6469
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 836–839
- Language:
- URL:
- https://aclanthology.org/W18-6469
- DOI:
- 10.18653/v1/W18-6469
- Cite (ACL):
- Daria Pylypenko and Raphael Rubino. 2018. DFKI-MLT System Description for the WMT18 Automatic Post-editing Task. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 836–839, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- DFKI-MLT System Description for the WMT18 Automatic Post-editing Task (Pylypenko & Rubino, WMT 2018)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W18-6469.pdf
- Data
- eSCAPE