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Lost in Back-Translation : Emotion Preservation in Neural Machine Translation
Troiano, Enrica; Klinger, Roman; Padó, Sebastian (2020): Lost in Back-Translation : Emotion Preservation in Neural Machine Translation, in: Donia Scott, Nuria Bell, Chengqing Zong, u. a. (Hrsg.), Proceedings of the 28th International Conference on Computational Linguistics, International Committee on Computational Linguistics, S. 4340–4354, doi: 10.18653/v1/2020.coling-main.384.
Faculty/Chair:
Author:
Title of the compilation:
Proceedings of the 28th International Conference on Computational Linguistics
Editors:
Scott, Donia
Bell, Nuria
Zong, Chengqing
Conference:
28th International Conference on Computational Linguistics ; Barcelona, Spain (Online)
Publisher Information:
Year of publication:
2020
Pages:
Language:
English
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.
GND Keywords: ;
Computerlinguistik
Emotion
Keywords:
Emotion Preservation
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RVK Classification:
Type:
Conferenceobject
Activation date:
March 14, 2024
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https://fis.uni-bamberg.de/handle/uniba/93906