Browse > Article
http://dx.doi.org/10.15207/JKCS.2020.11.5.001

Automatic Post Editing Research  

Park, Chan-Jun (Department of Computer Science and Engineering, Korea University)
Lim, Heui-Seok (Department of Computer Science and Engineering, Korea University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.5, 2020 , pp. 1-8 More about this Journal
Abstract
Machine translation refers to a system where a computer translates a source sentence into a target sentence. There are various subfields of machine translation. APE (Automatic Post Editing) is a subfield of machine translation that produces better translations by editing the output of machine translation systems. In other words, it means the process of correcting errors included in the translations generated by the machine translation system to make proofreading. Rather than changing the machine translation model, this is a research field to improve the translation quality by correcting the result sentence of the machine translation system. Since 2015, APE has been selected for the WMT Shaed Task. and the performance evaluation uses TER (Translation Error Rate). Due to this, various studies on the APE model have been published recently, and this paper deals with the latest research trends in the field of APE.
Keywords
Machine Translation; Automatic Post Editing; Deep Learning; Neural Machine Translation; Transformer;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bojar, O., Chatterjee, R., Federmann, C., Graham, Y., Haddow, B., Huck, M. & Negri, M. (2016, August). Findings of the 2016 conference on machine translation. In Proceedings of the First Conference on Machine Translation: 2, Shared Task Papers (pp. 131-198).
2 Ondrej, B., Chatterjee, R., Christian, F., Yvette, G., Barry, H., Matthias, H. & Negri, M. (2017). Findings of the 2017 conference on machine translation (wmt17). In Second Conference onMachine Translation (pp. 169-214). The Association for Computational Linguistics.
3 Allen, J. & Hogan, C. (2000, April). Toward the development of a post editing module for raw machine translation output: A controlled language perspective. In Third International Controlled Language Applications Workshop (CLAW-00) (pp. 62-71).
4 Tebbifakhr, A., Agrawal, R., Negri, M. & Turchi, M. (2018, October). Multi-source transformer with combined losses for automatic post editing. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers (pp. 846-852).
5 Libovicky, J., Helcl, J., Tlusty, M., Pecina, P. & Bojar, O. (2016). CUNI system for WMT16 automatic post-editing and multimodal translation tasks. arXiv preprint arXiv:1606.07481.
6 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).
7 H. Shin, W. K. Lee, Y. K. Kim & J. H. Lee. (2019). Research for the Decoder Structure of Multi-encoder Transformer-based Automatic Post-Editing Model. KIISE 2019, 634-636.
8 W. K. Lee, H. Shin, Y. K. Kim & J. H. Lee. (2019). Transformer-based Automatic Post-Editing with Effective Relation Modeling between Source and its Translations. KIISE 2019, 619-621.
9 Lee, W., Park, J., Go, B. H. & Lee, J. H. (2019). Transformer-based Automatic Post-Editing with a Context-Aware Encoding Approach for Multi-Source Inputs. arXiv preprint arXiv:1908.05679.
10 Chatterjee, R., Negri, M., Turchi, M., Blain, F., & Specia, L. (2018, March). Combining quality estimation and automatic post-editing to enhance machine translation output. In Proceedings of the 13th Conference of the Association for Machine Translation in the Americas, 1, (pp. 26-38).
11 Negri, M., Turchi, M., Chatterjee, R. & Bertoldi, N. (2018). ESCAPE: a large-scale synthetic corpus for automatic post-editing. arXiv preprint arXiv:1803.07274.
12 Simard, M., Goutte, C. & Isabelle, P. (2007, April). Statistical phrase-based post-editing. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference (pp. 508-515).
13 Snover, M., Dorr, B., Schwartz, R., Micciulla, L, & Makhoul, J. (2006, August). A study of translation edit rate with targeted human annotation. In Proceedings of association for machine translation in the Americas, 200(6) .
14 Papineni, K., Roukos, S., Ward, T. & Zhu, W. J. (2002, July). BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 311-318). Association for Computational Linguistics.
15 Junczys-Dowmunt, M. & Grundkiewicz, R. (2018). Ms-uedin submission to the wmt2018 ape shared task: Dual-source transformer for automatic post-editing. arXiv preprint arXiv:1809.00188.
16 Lopes, A. V., Farajian, M. A., Correia, G. M., Trenous, J. & Martins, A. F. (2019). Unbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing. arXiv preprint arXiv:1905.13068.
17 Correia, G. M. & Martins, A. F. (2019). A simple and effective approach to automatic post-editing with transfer learning. arXiv preprint arXiv:1906.06253.
18 Lee, W., Shin, J. & Lee, J. H. (2019, August). Transformer-based Automatic Post-Editing Model with Joint Encoder and Multi-source Attention of Decoder. In Proceedings of the Fourth Conference on Machine Translation, 3 (pp. 112-117).
19 J. H. Shin, Y. K. Kim & J. H. Lee. (2019) Transformer-based Automatic Post-Editing for Machine Translation KIISE Transactions on Computing Practices, 25(1), 64-69.   DOI
20 Pal, S., Herbig, N., Kruger, A. & van Genabith, J. (2018, October). A Transformer-Based Multi-Source Automatic Post-Editing System. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers (pp. 827-835).
21 Shin, J. & Lee, J. H. (2018, October). Multi-encoder Transformer Network for Automatic Post-Editing. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers (pp. 840-845).