@inproceedings{casacuberta-etal-2022-findings,
title = "Findings of the Word-Level {A}uto{C}ompletion Shared Task in {WMT} 2022",
author = "Casacuberta, Francisco and
Foster, George and
Huang, Guoping and
Koehn, Philipp and
Kovacs, Geza and
Liu, Lemao and
Shi, Shuming and
Watanabe, Taro and
Zong, Chengqing",
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.75",
pages = "812--820",
abstract = "Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.",
}
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<abstract>Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.</abstract>
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%0 Conference Proceedings
%T Findings of the Word-Level AutoCompletion Shared Task in WMT 2022
%A Casacuberta, Francisco
%A Foster, George
%A Huang, Guoping
%A Koehn, Philipp
%A Kovacs, Geza
%A Liu, Lemao
%A Shi, Shuming
%A Watanabe, Taro
%A Zong, Chengqing
%S Proceedings of the Seventh Conference on Machine Translation (WMT)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F casacuberta-etal-2022-findings
%X Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.
%U https://aclanthology.org/2022.wmt-1.75
%P 812-820
Markdown (Informal)
[Findings of the Word-Level AutoCompletion Shared Task in WMT 2022](https://aclanthology.org/2022.wmt-1.75) (Casacuberta et al., WMT 2022)
ACL
- Francisco Casacuberta, George Foster, Guoping Huang, Philipp Koehn, Geza Kovacs, Lemao Liu, Shuming Shi, Taro Watanabe, and Chengqing Zong. 2022. Findings of the Word-Level AutoCompletion Shared Task in WMT 2022. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 812–820, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.