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        <title>Understanding the Impact of UGC Specificities on Translation Quality</title>
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    <name type="personal">
        <namePart type="given">José</namePart>
        <namePart type="given">Carlos</namePart>
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            <title>Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)</title>
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            <namePart type="given">Alan</namePart>
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    <abstract>This work takes a critical look at the evaluation of user-generated content automatic translation, the well-known specificities of which raise many challenges for MT. Our analyses show that measuring the average-case performance using a standard metric on a UGC test set falls far short of giving a reliable image of the UGC translation quality. That is why we introduce a new data set for the evaluation of UGC translation in which UGC specificities have been manually annotated using a fine-grained typology. Using this data set, we conduct several experiments to measure the impact of different kinds of UGC specificities on translation quality, more precisely than previously possible.</abstract>
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    <identifier type="doi">10.18653/v1/2021.wnut-1.22</identifier>
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%0 Conference Proceedings
%T Understanding the Impact of UGC Specificities on Translation Quality
%A Rosales Núñez, José Carlos
%A Seddah, Djamé
%A Wisniewski, Guillaume
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F rosales-nunez-etal-2021-understanding
%X This work takes a critical look at the evaluation of user-generated content automatic translation, the well-known specificities of which raise many challenges for MT. Our analyses show that measuring the average-case performance using a standard metric on a UGC test set falls far short of giving a reliable image of the UGC translation quality. That is why we introduce a new data set for the evaluation of UGC translation in which UGC specificities have been manually annotated using a fine-grained typology. Using this data set, we conduct several experiments to measure the impact of different kinds of UGC specificities on translation quality, more precisely than previously possible.
%R 10.18653/v1/2021.wnut-1.22
%U https://aclanthology.org/2021.wnut-1.22
%U https://doi.org/10.18653/v1/2021.wnut-1.22
%P 189-198

Markdown (Informal)

[Understanding the Impact of UGC Specificities on Translation Quality](https://aclanthology.org/2021.wnut-1.22) (Rosales Núñez et al., WNUT 2021)

• Understanding the Impact of UGC Specificities on Translation Quality (Rosales Núñez et al., WNUT 2021)

ACL

• José Carlos Rosales Núñez, Djamé Seddah, and Guillaume Wisniewski. 2021. Understanding the Impact of UGC Specificities on Translation Quality. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 189–198, Online. Association for Computational Linguistics.