Attribute extraction aims to identify attribute names and the corresponding values from descriptive texts, which is the foundation for extensive downstream applications such as knowledge graph construction, search engines, and e-Commerce.In previous studies, attribute extraction is generally treated as a classification problem for predicting attribute types or a sequence tagging problem for labeling attribute values, where two paradigms, i.e., closed-world and open-world assumption, are involved.However, both of these paradigms have limitations in terms of real-world applications.And prior studies attempting to integrate these paradigms through ensemble, pipeline, and co-training models, still face challenges like cascading errors, high computational overhead, and difficulty in training.To address these existing problems, this paper presents Attribute Tree, a unified formulation for realworld attribute extraction application, where closed-world, open-world, and semi-open attribute extraction tasks are modeled uniformly.Then a text-to-tree generation model, AtTGen, is proposed to learn annotations from different scenarios efficiently and consistently.Experiments demonstrate that our proposed paradigm well covers various scenarios for real-world applications, and the model achieves state-ofthe-art, outperforming existing methods by a large margin on three datasets.Our code, pretrained model, and datasets are available at https://github.com/lsvih/AtTGen.

Document-level relation extraction (DocRE)aims to extract relations among entities within a document, which is crucial for applications like knowledge graph construction.Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications.To overcome this limitation, we propose TAG, a novel tableto-graph generation model for joint extraction of entities and relations at document-level.To enhance the learning of task dependencies, TAG induces a latent graph among mentions, with different types of edges indicating different task information, which is further broadcast with a relational graph convolutional network.To alleviate the error propagation problem, we adapt the hierarchical agglomerative clustering algorithm to back-propagate task information at decoding stage.Experiments on the benchmark dataset, DocRED, demonstrate that TAG surpasses previous methods by a large margin and achieves state-of-the-art results 1 .

Recent years have witnessed the transition from sentence-level to document-level in relation extraction (RE), with new formulation, new methods and new insights. Yet, the fundamental concept, mention, is not well-considered and well-defined. Current datasets usually use automatically-detected named entities as mentions, which leads to the missing reference problem. We show that such phenomenon hinders models' reasoning abilities. To address it, we propose to incorporate coreferences (e.g. pronouns and common nouns) into mentions, based on which we refine and re-annotate the widely-used DocRED benchmark as R-DocRED. We evaluate various methods and conduct thorough experiments to demonstrate the efficacy of our formula. Specifically, the results indicate that incorporating coreferences helps reduce the long-term dependencies, further improving models' robustness and generalization under adversarial and low-resource settings. The new dataset is made publicly available for future research.