Yuqing Yang


DORE: Document Ordered Relation Extraction based on Generative Framework
Qipeng Guo | Yuqing Yang | Hang Yan | Xipeng Qiu | Zheng Zhang
Findings of the Association for Computational Linguistics: EMNLP 2022

In recent years, there is a surge of generation-based information extraction work, which allows a more direct use of pre-trained language models and efficiently captures output dependencies. However, previous generative methods using lexical representation do not naturally fit document-level relation extraction (DocRE) where there are multiple entities and relational facts. In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models. We propose to generate a symbolic and ordered sequence from the relation matrix which is deterministic and easier for model to learn. Moreover, we design a parallel row generation method to process overlong target sequences. Besides, we introduce several negative sampling strategies to improve the performance with balanced signals. Experimental results on four datasets show that our proposed method can improve the performance of the generative DocRE models.


Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification
Pengfei Cao | Yubo Chen | Yuqing Yang | Kang Liu | Jun Zhao
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Event factuality indicates the degree of certainty about whether an event occurs in the real world. Existing studies mainly focus on identifying event factuality at sentence level, which easily leads to conflicts between different mentions of the same event. To this end, we study the problem of document-level event factuality identification, which determines the event factuality from the view of a document. For this task, we need to consider two important characteristics: Local Uncertainty and Global Structure, which can be utilized to improve performance. In this paper, we propose an Uncertain Local-to-Global Network (ULGN) to make use of these two characteristics. Specifically, we devise a Local Uncertainty Estimation module to model the uncertainty of local information. Moreover, we propose an Uncertain Information Aggregation module to leverage the global structure for integrating the local information. Experimental results demonstrate the effectiveness of our proposed method, outperforming the previous state-of-the-art model by 8.4% and 11.45% of F1 score on two widely used datasets.