# Towards Chapter-to-Chapter Context-Aware Literary Translation via Large Language Models

arXiv: https://arxiv.org/abs/2407.08978 · HTML: https://mlsanity.com/paper/2407.08978/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 37 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation](https://mlsanity.com/paper/2206.02369/) (2022)
- [A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models](https://mlsanity.com/paper/2309.11674/) (2023)
- [Large language models effectively leverage document-level context for literary translation, but critical errors persist](https://mlsanity.com/paper/2304.03245/) (2023)
- [Improving the Transformer Translation Model with Document-Level Context](https://mlsanity.com/paper/1810.03581/) (2018)
- [BigTranslate: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages](https://mlsanity.com/paper/2305.18098/) (2023)
- [Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions](https://mlsanity.com/paper/2305.15083/) (2023)
- [Measuring and Increasing Context Usage in Context-Aware Machine Translation](https://mlsanity.com/paper/2105.03482/) (2021)
- [Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature](https://mlsanity.com/paper/2210.14250/) (2022)
- [Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder](https://mlsanity.com/paper/2304.04052/) (2023)
- [BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models](https://mlsanity.com/paper/2306.10968/) (2023)
- [Rethinking Document-level Neural Machine Translation](https://mlsanity.com/paper/2010.08961/) (2020)
- [ChatGPT MT: Competitive for High- (but not Low-) Resource Languages](https://mlsanity.com/paper/2309.07423/) (2023)
- [Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models](https://mlsanity.com/paper/2103.17151/) (2021)
- [Look-back Decoding for Open-Ended Text Generation](https://mlsanity.com/paper/2305.13477/) (2023)
- [Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph Level](https://mlsanity.com/paper/2308.13506/) (2023)
- [Challenges in Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2305.13751/) (2023)
- [Exploring Paracrawl for Document-level Neural Machine Translation](https://mlsanity.com/paper/2304.10216/) (2023)

## Built on by (most cited first, 2 of 2)

- [LLMs Should Incorporate Explicit Mechanisms for Human Empathy](https://mlsanity.com/paper/2604.10557/) (2026)
- [PAUSE: Editable Strategy Artifacts for Long-Form Cultural Story Adaptation](https://mlsanity.com/paper/2608.28633/) (2026)

## Abstract (arXiv)

Discourse phenomena in existing document-level translation datasets are sparse, which has been a fundamental obstacle in the development of context-aware machine translation models. Moreover, most existing document-level corpora and context-aware machine translation methods rely on an unrealistic assumption on sentence-level alignments. To mitigate these issues, we first curate a novel dataset of Chinese-English literature, which consists of 160 books with intricate discourse structures. Then, we propose a more pragmatic and challenging setting for context-aware translation, termed chapter-to-chapter (Ch2Ch) translation, and investigate the performance of commonly-used machine translation models under this setting. Furthermore, we introduce a potential approach of finetuning large language models (LLMs) within the domain of Ch2Ch literary translation, yielding impressive improvements over baselines. Through our comprehensive analysis, we unveil that literary translation under the Ch2Ch setting is challenging in nature, with respect to both model learning methods and translation decoding algorithms.