Shiyao Cui


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Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction
Qian Li | Shu Guo | Cheng Ji | Xutan Peng | Shiyao Cui | Jianxin Li | Lihong Wang
Findings of the Association for Computational Linguistics: ACL 2023

Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues. Rich visual content is valuable for the MMRE task, but existing works cannot well model finer associations among different modalities, failing to capture the truly helpful visual information and thus limiting relation extraction performance. In this paper, we propose a novel MMRE framework to better capture the deeper correlations of text, entity pair, and image/objects, so as to mine more helpful information for the task, termed as DGF-PT. We first propose a prompt-based autoregressive encoder, which builds the associations of intra-modal and inter-modal features related to the task, respectively by entity-oriented and object-oriented prefixes. To better integrate helpful visual information, we design a dual-gated fusion module to distinguish the importance of image/objects and further enrich text representations. In addition, a generative decoder is introduced with entity type restriction on relations, better filtering out candidates. Extensive experiments conducted on the benchmark dataset show that our approach achieves excellent performance compared to strong competitors, even in the few-shot situation.


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Event Causality Extraction with Event Argument Correlations
Shiyao Cui | Jiawei Sheng | Xin Cong | Quangang Li | Tingwen Liu | Jinqiao Shi
Proceedings of the 29th International Conference on Computational Linguistics

Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding. However, the ECI task ignores crucial event structure and cause-effect causality component information, making it struggle for downstream applications. In this paper, we introduce a novel task, namely Event Causality Extraction (ECE), aiming to extract the cause-effect event causality pairs with their structured event information from plain texts. The ECE task is more challenging since each event can contain multiple event arguments, posing fine-grained correlations between events to decide the cause-effect event pair. Hence, we propose a method with a dual grid tagging scheme to capture the intra- and inter-event argument correlations for ECE. Further, we devise a event type-enhanced model architecture to realize the dual grid tagging scheme. Experiments demonstrate the effectiveness of our method, and extensive analyses point out several future directions for ECE.


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Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Xin Cong | Shiyao Cui | Bowen Yu | Tingwen Liu | Wang Yubin | Bin Wang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation
Shiyao Cui | Bowen Yu | Tingwen Liu | Zhenyu Zhang | Xuebin Wang | Jinqiao Shi
Findings of the Association for Computational Linguistics: EMNLP 2020

Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks. However, these methods usually ignore dependency label information, which conveys rich and useful linguistic knowledge for ED. In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected words through specific dependency types. Furthermore, to fully explore clues hidden from dependency edges, a node-aware edge update module is introduced, which refines the relation representations with contextual information. These two modules are complementary to each other and work in a mutual promotion way. We conduct experiments on the widely used ACE2005 dataset and the results show significant improvement over competitive baseline methods.