Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model. Prior works typically solve this task in the extract-then-classify or unified labeling manner. However, these methods either suffer from the redundant entity pairs, or ignore the important inner structure in the process of extracting entities and relations. To address these limitations, in this paper, we first decompose the joint extraction task into two inner-related subtasks, namely HE extraction and TER extraction. The former subtask is to distinguish all head-entities that may be involved with target relations, and the latter is to identify corresponding tail-entities and relations for each extracted head-entity. Next, these two subtasks are further deconstructed into several sequence labeling problems based on our proposed spanbased tagging scheme, which are conveniently solved by a hierarchical boundary tagger and a multi-span decoding algorithm. Owing to the reasonable decomposition strategy, our model can fully capture the semantic interdependency between different steps, as well as reduce noise from irrelevant entity pairs. Experimental results show that our method outperforms previous work by 5.6%, 17.2% and 3.7% (F1 score), achieving a new state-of-the-art on three public datasets.

Extracting relations from plain text is an important task with wide application. Most existing methods formulate it as a supervised problem and utilize one-hot hard labels as the sole target in training, neglecting the rich semantic information among relations. In this paper, we aim to explore the supervision with soft labels in relation extraction, which makes it possible to integrate prior knowledge. Specifically, a bipartite graph is first devised to discover type constraints between entities and relations based on the entire corpus. Then, we combine such type constraints with neural networks to achieve a knowledgeable model. Furthermore, this model is regarded as teacher to generate well-informed soft labels and guide the optimization of a student network via knowledge distillation. Besides, a multi-aspect attention mechanism is introduced to help student mine latent information from text. In this way, the enhanced student inherits the dark knowledge (e.g., type constraints and relevance among relations) from teacher, and directly serves the testing scenarios without any extra constraints. We conduct extensive experiments on the TACRED and SemEval datasets, the experimental results justify the effectiveness of our approach.

Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result. In this paper, we propose a novel graphbased model with Dual-tier Heterogeneous Graph (DHG) for document-level RE. In particular, DHG is composed of a structure modeling layer followed by a relation reasoning layer. The major advantage is that it is capable of not only capturing both the sequential and structural information of documents but also mixing them together to benefit for multi-hop reasoning and final decisionmaking. Furthermore, we employ Graph Neural Networks (GNNs) based message propagation strategy to accumulate information on DHG. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on two widely used datasets, and further analyses suggest that all the modules in our model are indispensable for document-level RE.

No abstract