@inproceedings{lee-etal-2021-intellicat,
title = "{I}ntelli{CAT}: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion",
author = "Lee, Dongjun and
Ahn, Junhyeong and
Park, Heesoo and
Jo, Jaemin",
editor = "Ji, Heng and
Park, Jong C. and
Xia, Rui",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-demo.2",
doi = "10.18653/v1/2021.acl-demo.2",
pages = "11--19",
abstract = "We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output. We leverage two quality estimation (QE) models at different granularities: sentence-level QE, to predict the quality of each machine-translated sentence, and word-level QE, to locate the parts of the machine-translated sentence that need correction. Additionally, we introduce a novel translation suggestion model conditioned on both the left and right contexts, providing alternatives for specific words or phrases for correction. Finally, with word alignments, IntelliCAT automatically preserves the original document{'}s styles in the translated document. The experimental results show that post-editing based on the proposed QE and translation suggestions can significantly improve translation quality. Furthermore, a user study reveals that three features provided in IntelliCAT significantly accelerate the post-editing task, achieving a 52.9{\%} speedup in translation time compared to translating from scratch. The interface is publicly available at \url{https://intellicat.beringlab.com/}.",
}
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<abstract>We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output. We leverage two quality estimation (QE) models at different granularities: sentence-level QE, to predict the quality of each machine-translated sentence, and word-level QE, to locate the parts of the machine-translated sentence that need correction. Additionally, we introduce a novel translation suggestion model conditioned on both the left and right contexts, providing alternatives for specific words or phrases for correction. Finally, with word alignments, IntelliCAT automatically preserves the original document’s styles in the translated document. The experimental results show that post-editing based on the proposed QE and translation suggestions can significantly improve translation quality. Furthermore, a user study reveals that three features provided in IntelliCAT significantly accelerate the post-editing task, achieving a 52.9% speedup in translation time compared to translating from scratch. The interface is publicly available at https://intellicat.beringlab.com/.</abstract>
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%0 Conference Proceedings
%T IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion
%A Lee, Dongjun
%A Ahn, Junhyeong
%A Park, Heesoo
%A Jo, Jaemin
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F lee-etal-2021-intellicat
%X We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output. We leverage two quality estimation (QE) models at different granularities: sentence-level QE, to predict the quality of each machine-translated sentence, and word-level QE, to locate the parts of the machine-translated sentence that need correction. Additionally, we introduce a novel translation suggestion model conditioned on both the left and right contexts, providing alternatives for specific words or phrases for correction. Finally, with word alignments, IntelliCAT automatically preserves the original document’s styles in the translated document. The experimental results show that post-editing based on the proposed QE and translation suggestions can significantly improve translation quality. Furthermore, a user study reveals that three features provided in IntelliCAT significantly accelerate the post-editing task, achieving a 52.9% speedup in translation time compared to translating from scratch. The interface is publicly available at https://intellicat.beringlab.com/.
%R 10.18653/v1/2021.acl-demo.2
%U https://aclanthology.org/2021.acl-demo.2
%U https://doi.org/10.18653/v1/2021.acl-demo.2
%P 11-19
Markdown (Informal)
[IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion](https://aclanthology.org/2021.acl-demo.2) (Lee et al., ACL-IJCNLP 2021)
ACL