mlsanityData to 14 Oct 2026

Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translation

arXiv 2412.11732 ↗First posted 16 Dec 2024cs.CLcited by 15

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Abstract (arXiv)

Following last year, we have continued to host the WMT translation shared task this year, the second edition of the Discourse-Level Literary Translation. We focus on three language directions: Chinese-English, Chinese-German, and Chinese-Russian, with the latter two ones newly added. 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. We release data, system outputs, and leaderboard at https://www2.statmt.org/wmt24/literary-translation-task.html.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 10 of its 28 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

10 of 28 references.

References · 10

  1. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  2. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  3. 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
  4. 2024(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Textscited by 49
  5. 2017Exploiting Cross-Sentence Context for Neural Machine Translationcited by 48
  6. 2023Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMscited by 25
  7. 2019One Model to Learn Both: Zero Pronoun Prediction and Translationcited by 14
  8. 2023Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modellingcited by 10
  9. 2024Context-aware and Style-related Incremental Decoding framework for Discourse-Level Literary Translationcited by 5
  10. 2024Marco-LLM: Bridging Languages via Massive Multilingual Training for Cross-Lingual Enhancementcited by 4

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Cited by year (2026 to 14 Oct)

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

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  1. 2025New Trends for Modern Machine Translation with Large Reasoning Modelscited by 17
  2. 2025Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translationcited by 8
  3. 2025DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel Translationcited by 4
  4. 2025Steering Large Language Models for Machine Translation Personalizationcited by 3
  5. 2025DiscoX: Benchmarking Discourse-Level Translation task in Expert Domainscited by 3
  6. 2025Test-Time Alignment for Large Language Models via Textual Model Predictive Controlcited by 2
  7. 2025GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translationcited by 2
  8. 2025Discourse Graph Guided Document Translation with Large Language Modelscited by 1
  9. 2026LLMs Should Incorporate Explicit Mechanisms for Human Empathycited by 0
  10. 2026HardMTBench: Stress-Testing Chinese-English Translation on Knowledge-Intensive Domainscited by 0
  11. 2026Comparative Evaluation of Machine Translation Systems on Images with Textcited by 0
  12. 2026Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translationcited by 0
3 more
  1. 2026PAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translationcited by 0
  2. 2026PAUSE: Editable Strategy Artifacts for Long-Form Cultural Story Adaptationcited by 0
  3. 2026Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunkingcited by 0