@inproceedings{wang-etal-2024-findings,
title = "Findings of the {WMT} 2024 Shared Task on Discourse-Level Literary Translation",
author = "Wang, Longyue and
Liu, Siyou and
Lyu, Chenyang and
Jiao, Wenxiang and
Wang, Xing and
Xu, Jiahao and
Tu, Zhaopeng and
Gu, Yan and
Chen, Weiyu and
Wu, Minghao and
Zhou, Liting and
Koehn, Philipp and
Way, Andy and
Yuan, Yulin",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Ninth Conference on Machine Translation",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wmt-1.58",
pages = "699--700",
abstract = "Translating literary works has perennially stood as an elusive dream in machine translation (MT), a journey steeped in intricate challenges. To foster progress in this domain, we hold a new shared task at WMT 2023, the second edition of the \textit{ Discourse-Level Literary Translation}. First, we (Tencent AI Lab and China Literature Ltd.) release a copyrighted and document-level Chinese-English web novel corpus. Furthermore, we put forth an industry-endorsed criteria to guide human evaluation process. This year, we totally received 10 submissions from 5 academia and industry teams. We employ both automatic and human evaluations to measure the performance of the submitted systems. The official ranking of the systems is based on the overall human judgments. In addition, our extensive analysis reveals a series of interesting findings on literary and discourse-aware MT. We release data, system outputs, and leaderboard at \url{https://www2.statmt.org/wmt24/literary-translation-task.html}.",
}
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<abstract>Translating literary works has perennially stood as an elusive dream in machine translation (MT), a journey steeped in intricate challenges. To foster progress in this domain, we hold a new shared task at WMT 2023, the second edition of the Discourse-Level Literary Translation. First, we (Tencent AI Lab and China Literature Ltd.) release a copyrighted and document-level Chinese-English web novel corpus. Furthermore, we put forth an industry-endorsed criteria to guide human evaluation process. This year, we totally received 10 submissions from 5 academia and industry teams. We employ both automatic and human evaluations to measure the performance of the submitted systems. The official ranking of the systems is based on the overall human judgments. In addition, our extensive analysis reveals a series of interesting findings on literary and discourse-aware MT. We release data, system outputs, and leaderboard at https://www2.statmt.org/wmt24/literary-translation-task.html.</abstract>
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%0 Conference Proceedings
%T Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translation
%A Wang, Longyue
%A Liu, Siyou
%A Lyu, Chenyang
%A Jiao, Wenxiang
%A Wang, Xing
%A Xu, Jiahao
%A Tu, Zhaopeng
%A Gu, Yan
%A Chen, Weiyu
%A Wu, Minghao
%A Zhou, Liting
%A Koehn, Philipp
%A Way, Andy
%A Yuan, Yulin
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-findings
%X Translating literary works has perennially stood as an elusive dream in machine translation (MT), a journey steeped in intricate challenges. To foster progress in this domain, we hold a new shared task at WMT 2023, the second edition of the Discourse-Level Literary Translation. First, we (Tencent AI Lab and China Literature Ltd.) release a copyrighted and document-level Chinese-English web novel corpus. Furthermore, we put forth an industry-endorsed criteria to guide human evaluation process. This year, we totally received 10 submissions from 5 academia and industry teams. We employ both automatic and human evaluations to measure the performance of the submitted systems. The official ranking of the systems is based on the overall human judgments. In addition, our extensive analysis reveals a series of interesting findings on literary and discourse-aware MT. We release data, system outputs, and leaderboard at https://www2.statmt.org/wmt24/literary-translation-task.html.
%U https://aclanthology.org/2024.wmt-1.58
%P 699-700
Markdown (Informal)
[Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translation](https://aclanthology.org/2024.wmt-1.58) (Wang et al., WMT 2024)
ACL
- Longyue Wang, Siyou Liu, Chenyang Lyu, Wenxiang Jiao, Xing Wang, Jiahao Xu, Zhaopeng Tu, Yan Gu, Weiyu Chen, Minghao Wu, Liting Zhou, Philipp Koehn, Andy Way, and Yulin Yuan. 2024. Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translation. In Proceedings of the Ninth Conference on Machine Translation, pages 699–700, Miami, Florida, USA. Association for Computational Linguistics.