Abstract
Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world. An important part of communication, however, takes place at the non-propositional level (e.g., politeness, formality, emotions), and it is far from clear whether current MT methods properly translate this information. This paper investigates the specific hypothesis that the non-propositional level of emotions is at least partially lost in MT. We carry out a number of experiments in a back-translation setup and establish that (1) emotions are indeed partially lost during translation; (2) this tendency can be reversed almost completely with a simple re-ranking approach informed by an emotion classifier, taking advantage of diversity in the n-best list; (3) the re-ranking approach can also be applied to change emotions, obtaining a model for emotion style transfer. An in-depth qualitative analysis reveals that there are recurring linguistic changes through which emotions are toned down or amplified, such as change of modality.- Anthology ID:
- 2020.coling-main.384
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4340–4354
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.384
- DOI:
- 10.18653/v1/2020.coling-main.384
- Cite (ACL):
- Enrica Troiano, Roman Klinger, and Sebastian Padó. 2020. Lost in Back-Translation: Emotion Preservation in Neural Machine Translation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4340–4354, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Lost in Back-Translation: Emotion Preservation in Neural Machine Translation (Troiano et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.coling-main.384.pdf
- Data
- ISEAR