@inproceedings{troiano-etal-2020-lost,
title = "Lost in Back-Translation: Emotion Preservation in Neural Machine Translation",
author = "Troiano, Enrica and
Klinger, Roman and
Pad{\'o}, Sebastian",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.384/",
doi = "10.18653/v1/2020.coling-main.384",
pages = "4340--4354",
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."
}
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%0 Conference Proceedings
%T Lost in Back-Translation: Emotion Preservation in Neural Machine Translation
%A Troiano, Enrica
%A Klinger, Roman
%A Padó, Sebastian
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F troiano-etal-2020-lost
%X 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.
%R 10.18653/v1/2020.coling-main.384
%U https://aclanthology.org/2020.coling-main.384/
%U https://doi.org/10.18653/v1/2020.coling-main.384
%P 4340-4354
Markdown (Informal)
[Lost in Back-Translation: Emotion Preservation in Neural Machine Translation](https://aclanthology.org/2020.coling-main.384/) (Troiano et al., COLING 2020)
ACL