Lost in Back-Translation: Emotion Preservation in Neural Machine Translation

Enrica Troiano, Roman Klinger, Sebastian Padó


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)
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.384.pdf
Data
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