@inproceedings{gupta-etal-2022-interactive,
title = "Interactive Post-Editing for Verbosity Controlled Translation",
author = "Gupta, Prabhakar and
Nelakanti, Anil and
Berry, Grant M. and
Sharma, Abhishek",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.454",
pages = "5119--5128",
abstract = "We explore Interactive Post-Editing (IPE) models for human-in-loop translation to help correct translation errors and rephrase it with a desired style variation. We specifically study verbosity for style variations and build on top of multi-source transformers that can read source and hypothesis to improve the latter with user inputs. Token-level interaction inputs for error corrections and length interaction inputs for verbosity control are used by the model to generate a suitable translation. We report BERTScore to evaluate semantic quality with other relevant metrics for translations from English to German, French and Spanish languages. Our model achieves superior BERTScore over state-of-the-art machine translation models while maintaining the desired token-level and verbosity preference.",
}
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<abstract>We explore Interactive Post-Editing (IPE) models for human-in-loop translation to help correct translation errors and rephrase it with a desired style variation. We specifically study verbosity for style variations and build on top of multi-source transformers that can read source and hypothesis to improve the latter with user inputs. Token-level interaction inputs for error corrections and length interaction inputs for verbosity control are used by the model to generate a suitable translation. We report BERTScore to evaluate semantic quality with other relevant metrics for translations from English to German, French and Spanish languages. Our model achieves superior BERTScore over state-of-the-art machine translation models while maintaining the desired token-level and verbosity preference.</abstract>
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%0 Conference Proceedings
%T Interactive Post-Editing for Verbosity Controlled Translation
%A Gupta, Prabhakar
%A Nelakanti, Anil
%A Berry, Grant M.
%A Sharma, Abhishek
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F gupta-etal-2022-interactive
%X We explore Interactive Post-Editing (IPE) models for human-in-loop translation to help correct translation errors and rephrase it with a desired style variation. We specifically study verbosity for style variations and build on top of multi-source transformers that can read source and hypothesis to improve the latter with user inputs. Token-level interaction inputs for error corrections and length interaction inputs for verbosity control are used by the model to generate a suitable translation. We report BERTScore to evaluate semantic quality with other relevant metrics for translations from English to German, French and Spanish languages. Our model achieves superior BERTScore over state-of-the-art machine translation models while maintaining the desired token-level and verbosity preference.
%U https://aclanthology.org/2022.coling-1.454
%P 5119-5128
Markdown (Informal)
[Interactive Post-Editing for Verbosity Controlled Translation](https://aclanthology.org/2022.coling-1.454) (Gupta et al., COLING 2022)
ACL
- Prabhakar Gupta, Anil Nelakanti, Grant M. Berry, and Abhishek Sharma. 2022. Interactive Post-Editing for Verbosity Controlled Translation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5119–5128, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.