  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.
