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 Qingsong Ma Chenyang Lyu Liting Zhou Chao-Hong Liu Yufeng Ma Weiyu Chen Yvette Graham Bonnie Webber Philipp Koehn Andy Way Yulin Yuan Shuming Shi
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.