@inproceedings{liu-etal-2023-findings,
title = "Findings of the Word-Level {A}uto{C}ompletion Shared Task in {WMT} 2023",
author = "Liu, Lemao and
Casacuberta, Francisco and
Foster, George and
Huang, Guoping and
Koehn, Philipp and
Kovacs, Geza and
Shi, Shuming and
Watanabe, Taro and
Zong, Chengqing",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.53",
doi = "10.18653/v1/2023.wmt-1.53",
pages = "654--662",
abstract = "This paper presents the overview of the second Word-Level autocompletion (WLAC) shared task for computer-aided translation, which aims to automatically complete a target word given a translation context including a human typed character sequence. We largely adhere to the settings of the previous round of the shared task, but with two main differences: 1) The typed character sequence is obtained from the typing process of human translators to demonstrate system performance under real-world scenarios when preparing some type of testing examples; 2) We conduct a thorough analysis on the results of the submitted systems from three perspectives. From the experimental results, we observe that translation tasks are helpful to improve the performance of WLAC models. Additionally, our further analysis shows that the semantic error accounts for a significant portion of all errors, and thus it would be promising to take this type of errors into account in future.",
}
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<abstract>This paper presents the overview of the second Word-Level autocompletion (WLAC) shared task for computer-aided translation, which aims to automatically complete a target word given a translation context including a human typed character sequence. We largely adhere to the settings of the previous round of the shared task, but with two main differences: 1) The typed character sequence is obtained from the typing process of human translators to demonstrate system performance under real-world scenarios when preparing some type of testing examples; 2) We conduct a thorough analysis on the results of the submitted systems from three perspectives. From the experimental results, we observe that translation tasks are helpful to improve the performance of WLAC models. Additionally, our further analysis shows that the semantic error accounts for a significant portion of all errors, and thus it would be promising to take this type of errors into account in future.</abstract>
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%0 Conference Proceedings
%T Findings of the Word-Level AutoCompletion Shared Task in WMT 2023
%A Liu, Lemao
%A Casacuberta, Francisco
%A Foster, George
%A Huang, Guoping
%A Koehn, Philipp
%A Kovacs, Geza
%A Shi, Shuming
%A Watanabe, Taro
%A Zong, Chengqing
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liu-etal-2023-findings
%X This paper presents the overview of the second Word-Level autocompletion (WLAC) shared task for computer-aided translation, which aims to automatically complete a target word given a translation context including a human typed character sequence. We largely adhere to the settings of the previous round of the shared task, but with two main differences: 1) The typed character sequence is obtained from the typing process of human translators to demonstrate system performance under real-world scenarios when preparing some type of testing examples; 2) We conduct a thorough analysis on the results of the submitted systems from three perspectives. From the experimental results, we observe that translation tasks are helpful to improve the performance of WLAC models. Additionally, our further analysis shows that the semantic error accounts for a significant portion of all errors, and thus it would be promising to take this type of errors into account in future.
%R 10.18653/v1/2023.wmt-1.53
%U https://aclanthology.org/2023.wmt-1.53
%U https://doi.org/10.18653/v1/2023.wmt-1.53
%P 654-662
Markdown (Informal)
[Findings of the Word-Level AutoCompletion Shared Task in WMT 2023](https://aclanthology.org/2023.wmt-1.53) (Liu et al., WMT 2023)
ACL
- Lemao Liu, Francisco Casacuberta, George Foster, Guoping Huang, Philipp Koehn, Geza Kovacs, Shuming Shi, Taro Watanabe, and Chengqing Zong. 2023. Findings of the Word-Level AutoCompletion Shared Task in WMT 2023. In Proceedings of the Eighth Conference on Machine Translation, pages 654–662, Singapore. Association for Computational Linguistics.