@inproceedings{wang-etal-2023-findings,
title = "Findings of the {WMT} 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of {LLM}s",
author = "Wang, Longyue and
Tu, Zhaopeng and
Gu, Yan and
Liu, Siyou and
Yu, Dian and
Ma, Qingsong and
Lyu, Chenyang and
Zhou, Liting and
Liu, Chao-Hong and
Ma, Yufeng and
Chen, Weiyu and
Graham, Yvette and
Webber, Bonnie and
Koehn, Philipp and
Way, Andy and
Yuan, Yulin and
Shi, Shuming",
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-1.3",
doi = "10.18653/v1/2023.wmt-1.3",
pages = "55--67",
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 first 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 14 submissions from 7 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 http://www2.statmt.org/wmt23/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 first 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 14 submissions from 7 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 http://www2.statmt.org/wmt23/literary-translation-task.html.</abstract>
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%0 Conference Proceedings
%T Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs
%A Wang, Longyue
%A Tu, Zhaopeng
%A Gu, Yan
%A Liu, Siyou
%A Yu, Dian
%A Ma, Qingsong
%A Lyu, Chenyang
%A Zhou, Liting
%A Liu, Chao-Hong
%A Ma, Yufeng
%A Chen, Weiyu
%A Graham, Yvette
%A Webber, Bonnie
%A Koehn, Philipp
%A Way, Andy
%A Yuan, Yulin
%A Shi, Shuming
%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 wang-etal-2023-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 first 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 14 submissions from 7 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 http://www2.statmt.org/wmt23/literary-translation-task.html.
%R 10.18653/v1/2023.wmt-1.3
%U https://aclanthology.org/2023.wmt-1.3
%U https://doi.org/10.18653/v1/2023.wmt-1.3
%P 55-67
Markdown (Informal)
[Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs](https://aclanthology.org/2023.wmt-1.3) (Wang et al., WMT 2023)
ACL
- Longyue Wang, Zhaopeng Tu, Yan Gu, Siyou Liu, Dian Yu, Qingsong Ma, Chenyang Lyu, Liting Zhou, Chao-Hong Liu, Yufeng Ma, Weiyu Chen, Yvette Graham, Bonnie Webber, Philipp Koehn, Andy Way, Yulin Yuan, and Shuming Shi. 2023. Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs. In Proceedings of the Eighth Conference on Machine Translation, pages 55–67, Singapore. Association for Computational Linguistics.