Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs

Longyue Wang, Zhaopeng Tu, Yan Gu, Siyou Liu, Dian Yu +12 more
11/6/2023
cs.CLcs.AI

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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.

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@article{wang2023findings,
  title  = {Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs},
  author = {Longyue Wang and Zhaopeng Tu and Yan Gu and Siyou Liu and Dian Yu and Qingsong Ma and Chenyang Lyu and Liting Zhou and Chao-Hong Liu and Yufeng Ma and Weiyu Chen and Yvette Graham and Bonnie Webber and Philipp Koehn and Andy Way and Yulin Yuan and Shuming Shi},
  year   = {2023},
  eprint = {2311.03127},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.03127v1}
}

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