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

arXiv: https://arxiv.org/abs/2412.11732 · HTML: https://mlsanity.com/paper/2412.11732/

## 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 (most cited first, 10 of 10)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts](https://mlsanity.com/paper/2405.11804/) (2024)
- [Exploiting Cross-Sentence Context for Neural Machine Translation](https://mlsanity.com/paper/1704.04347/) (2017)
- [Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs](https://mlsanity.com/paper/2311.03127/) (2023)
- [One Model to Learn Both: Zero Pronoun Prediction and Translation](https://mlsanity.com/paper/1909.00369/) (2019)
- [Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling](https://mlsanity.com/paper/2307.08074/) (2023)
- [Context-aware and Style-related Incremental Decoding framework for Discourse-Level Literary Translation](https://mlsanity.com/paper/2409.16539/) (2024)
- [Marco-LLM: Bridging Languages via Massive Multilingual Training for Cross-Lingual Enhancement](https://mlsanity.com/paper/2412.04003/) (2024)

## Built on by (most cited first, 15 of 15)

- [New Trends for Modern Machine Translation with Large Reasoning Models](https://mlsanity.com/paper/2503.10351/) (2025)
- [Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation](https://mlsanity.com/paper/2504.01919/) (2025)
- [DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel Translation](https://mlsanity.com/paper/2510.09116/) (2025)
- [Steering Large Language Models for Machine Translation Personalization](https://mlsanity.com/paper/2505.16612/) (2025)
- [DiscoX: Benchmarking Discourse-Level Translation task in Expert Domains](https://mlsanity.com/paper/2511.10984/) (2025)
- [Test-Time Alignment for Large Language Models via Textual Model Predictive Control](https://mlsanity.com/paper/2502.20795/) (2025)
- [GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation](https://mlsanity.com/paper/2507.03311/) (2025)
- [Discourse Graph Guided Document Translation with Large Language Models](https://mlsanity.com/paper/2511.07230/) (2025)
- [LLMs Should Incorporate Explicit Mechanisms for Human Empathy](https://mlsanity.com/paper/2604.10557/) (2026)
- [HardMTBench: Stress-Testing Chinese-English Translation on Knowledge-Intensive Domains](https://mlsanity.com/paper/2605.28315/) (2026)
- [Comparative Evaluation of Machine Translation Systems on Images with Text](https://mlsanity.com/paper/2605.29476/) (2026)
- [Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation](https://mlsanity.com/paper/2607.29287/) (2026)
- [PAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translation](https://mlsanity.com/paper/2608.03077/) (2026)
- [PAUSE: Editable Strategy Artifacts for Long-Form Cultural Story Adaptation](https://mlsanity.com/paper/2608.28633/) (2026)
- [Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking](https://mlsanity.com/paper/2609.12674/) (2026)

## 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.