Title: GuoFeng: A Discourse-aware Evaluation Benchmark for Language Understanding, Translation and Generation. Abstract: Modeling discourse -- the linguistic phenomena that go beyond individual sentences, is a fundamental and challenging problem in natural language processing (NLP). However, existing evaluation benchmarks mainly focus on the evaluation of inter-sentence properties and overlook important discourse phenomena that cross sentences. To bridge the gap, we propose a GuoFeng benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks, covering understanding, translation, and generation. GuoFeng consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena (e.g. cohesion and coherence) in Chinese and/or English. For linguistic analysis, we also propose a diagnostic test suite that can examine whether the target models learn discourse knowledge. We evaluate 17 general- and in-domain models based on Transformer and advanced pre-training architectures, showing that fine-grained pretraining based on document-level training data consistently improves the modeling of discourse information. We will release the datasets, pretrained models, and leaderboard, which we hope can significantly facilitate research in this field.