mlsanityData to 14 Oct 2026

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

arXiv 2311.03127 ↗First posted 6 Nov 2023cs.CL cs.AIcited by 25

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Abstract (arXiv)

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.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 26 of its 47 references that are in the map. First posted on arXiv 6 Nov 2023.

Builds on

26 of 47 references.

References · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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  5. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
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  12. 2017Exploiting Cross-Sentence Context for Neural Machine Translationcited by 48
14 more
  1. 2022Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literaturecited by 45
  2. 2021Progressive Multi-Granularity Training for Non-Autoregressive Translationcited by 25
  3. 2022Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?cited by 24
  4. 2023A Paradigm Shift: The Future of Machine Translation Lies with Large Language Modelscited by 19
  5. 2021TranSmart: A Practical Interactive Machine Translation Systemcited by 17
  6. 2018Translating Pro-Drop Languages with Reconstruction Modelscited by 14
  7. 2019One Model to Learn Both: Zero Pronoun Prediction and Translationcited by 14
  8. 2018What Level of Quality can Neural Machine Translation Attain on Literary Text?cited by 11
  9. 2023Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modellingcited by 10
  10. 2016A Novel Approach to Dropped Pronoun Translationcited by 9
  11. 2023A Survey on Zero Pronoun Translationcited by 9
  12. 2018Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanismcited by 6
  13. 2023Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenationcited by 6
  14. 2023Revisiting Non-Autoregressive Translation at Scalecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 10; 2025: 8; 2026: 7.

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  1. 2024(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Textscited by 49
  2. 2024With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generationcited by 37
  3. 2024DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memorycited by 27
  4. 2024How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMscited by 23
  5. 2024Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Modelscited by 17
  6. 2024Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translationcited by 15
  7. 2024Anchor-based Large Language Modelscited by 12
  8. 2025Multilingual Contextualization of Large Language Models for Document-Level Machine Translationcited by 11
  9. 2024Creative and Context-Aware Translation of East Asian Idioms with GPT-4cited by 10
  10. 2025LiTransProQA: an LLM-based Literary Translation evaluation metric with Professional Question Answeringcited by 10
  11. 2025Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translationcited by 8
  12. 2024M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translationcited by 4
13 more
  1. 2025DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel Translationcited by 4
  2. 2025Steering Large Language Models for Machine Translation Personalizationcited by 3
  3. 2025DiscoX: Benchmarking Discourse-Level Translation task in Expert Domainscited by 3
  4. 2024A 2-step Framework for Automated Literary Translation Evaluation: Its Promises and Pitfallscited by 2
  5. 2025GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translationcited by 2
  6. 2025Building Large-Scale English-Romanian Literary Translation Resources with Open Modelscited by 2
  7. 2026Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selectioncited by 1
  8. 2026Automated evaluation of LLMs for effective machine translation of Mandarin Chinese to Englishcited by 0
  9. 2026LLMs Should Incorporate Explicit Mechanisms for Human Empathycited by 0
  10. 2026Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translationcited by 0
  11. 2026PAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translationcited by 0
  12. 2026PAUSE: Editable Strategy Artifacts for Long-Form Cultural Story Adaptationcited by 0
  13. 2026Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunkingcited by 0