Abstract
Neural machine translation systems with subword vocabularies are capable of translating or copying unknown words. In this work, we show that they learn to copy words based on both the context in which the words appear as well as features of the words themselves. In contexts that are particularly copy-prone, they even copy words that they have already learned they should translate. We examine the influence of context and subword features on this and other types of copying behavior.- Anthology ID:
- D18-1339
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3034–3041
- Language:
- URL:
- https://aclanthology.org/D18-1339
- DOI:
- 10.18653/v1/D18-1339
- Cite (ACL):
- Rebecca Knowles and Philipp Koehn. 2018. Context and Copying in Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3034–3041, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Context and Copying in Neural Machine Translation (Knowles & Koehn, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-1339.pdf