@inproceedings{saadany-etal-2023-analysing,
title = "Analysing Mistranslation of Emotions in Multilingual Tweets by Online {MT} Tools",
author = "Saadany, Hadeel and
Orasan, Constantin and
Quintana, Rocio Caro and
Carmo, Felix Do and
Zilio, Leonardo",
editor = "Nurminen, Mary and
Brenner, Judith and
Koponen, Maarit and
Latomaa, Sirkku and
Mikhailov, Mikhail and
Schierl, Frederike and
Ranasinghe, Tharindu and
Vanmassenhove, Eva and
Vidal, Sergi Alvarez and
Aranberri, Nora and
Nunziatini, Mara and
Escart{\'\i}n, Carla Parra and
Forcada, Mikel and
Popovic, Maja and
Scarton, Carolina and
Moniz, Helena",
booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2023",
address = "Tampere, Finland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2023.eamt-1.27",
pages = "275--284",
abstract = "It is common for websites that contain User-Generated Text (UGT) to provide an automatic translation option to reach out to their linguistically diverse users. In such scenarios, the process of translating the users{'} emotions is entirely automatic with no human intervention, neither for post-editing, nor for accuracy checking. In this paper, we assess whether automatic translation tools can be a successful real-life utility in transferring emotion in multilingual tweets. Our analysis shows that the mistranslation of the source tweet can lead to critical errors where the emotion is either completely lost or flipped to an opposite sentiment. We identify linguistic phenomena specific to Twitter data which pose a challenge in translation of emotions and show how frequent these features are in different language pairs. We also show that commonly-used quality metrics can lend false confidence in the performance of online MT tools specifically when the source emotion is distorted in telegraphic messages such as tweets.",
}
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<namePart type="given">Eva</namePart>
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<abstract>It is common for websites that contain User-Generated Text (UGT) to provide an automatic translation option to reach out to their linguistically diverse users. In such scenarios, the process of translating the users’ emotions is entirely automatic with no human intervention, neither for post-editing, nor for accuracy checking. In this paper, we assess whether automatic translation tools can be a successful real-life utility in transferring emotion in multilingual tweets. Our analysis shows that the mistranslation of the source tweet can lead to critical errors where the emotion is either completely lost or flipped to an opposite sentiment. We identify linguistic phenomena specific to Twitter data which pose a challenge in translation of emotions and show how frequent these features are in different language pairs. We also show that commonly-used quality metrics can lend false confidence in the performance of online MT tools specifically when the source emotion is distorted in telegraphic messages such as tweets.</abstract>
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%0 Conference Proceedings
%T Analysing Mistranslation of Emotions in Multilingual Tweets by Online MT Tools
%A Saadany, Hadeel
%A Orasan, Constantin
%A Quintana, Rocio Caro
%A Carmo, Felix Do
%A Zilio, Leonardo
%Y Nurminen, Mary
%Y Brenner, Judith
%Y Koponen, Maarit
%Y Latomaa, Sirkku
%Y Mikhailov, Mikhail
%Y Schierl, Frederike
%Y Ranasinghe, Tharindu
%Y Vanmassenhove, Eva
%Y Vidal, Sergi Alvarez
%Y Aranberri, Nora
%Y Nunziatini, Mara
%Y Escartín, Carla Parra
%Y Forcada, Mikel
%Y Popovic, Maja
%Y Scarton, Carolina
%Y Moniz, Helena
%S Proceedings of the 24th Annual Conference of the European Association for Machine Translation
%D 2023
%8 June
%I European Association for Machine Translation
%C Tampere, Finland
%F saadany-etal-2023-analysing
%X It is common for websites that contain User-Generated Text (UGT) to provide an automatic translation option to reach out to their linguistically diverse users. In such scenarios, the process of translating the users’ emotions is entirely automatic with no human intervention, neither for post-editing, nor for accuracy checking. In this paper, we assess whether automatic translation tools can be a successful real-life utility in transferring emotion in multilingual tweets. Our analysis shows that the mistranslation of the source tweet can lead to critical errors where the emotion is either completely lost or flipped to an opposite sentiment. We identify linguistic phenomena specific to Twitter data which pose a challenge in translation of emotions and show how frequent these features are in different language pairs. We also show that commonly-used quality metrics can lend false confidence in the performance of online MT tools specifically when the source emotion is distorted in telegraphic messages such as tweets.
%U https://aclanthology.org/2023.eamt-1.27
%P 275-284
Markdown (Informal)
[Analysing Mistranslation of Emotions in Multilingual Tweets by Online MT Tools](https://aclanthology.org/2023.eamt-1.27) (Saadany et al., EAMT 2023)
ACL