Abstract
We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation. We propose a simple and effective way of utilizing such feedback in NMT training. We demonstrate how the common machine translation problem of domain mismatch between training and deployment can be reduced solely based on chunk-level user feedback. We conduct a series of simulation experiments to test the effectiveness of the proposed method. Our results show that chunk-level feedback outperforms sentence based feedback by up to 2.61% BLEU absolute.- Anthology ID:
- P18-2052
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 326–331
- Language:
- URL:
- https://aclanthology.org/P18-2052
- DOI:
- 10.18653/v1/P18-2052
- Cite (ACL):
- Pavel Petrushkov, Shahram Khadivi, and Evgeny Matusov. 2018. Learning from Chunk-based Feedback in Neural Machine Translation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 326–331, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Learning from Chunk-based Feedback in Neural Machine Translation (Petrushkov et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P18-2052.pdf