Xueyang Qin


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Event Representation Learning with Multi-Grained Contrastive Learning and Triple-Mixture of Experts
Tianqi Hu | Lishuang Li | Xueyang Qin | Yubo Feng
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Event representation learning plays a crucial role in numerous natural language processing (NLP) tasks, as it facilitates the extraction of semantic features associated with events. Current methods of learning event representation based on contrastive learning processes positive examples with single-grain random masked language model (MLM), but fall short in learn information inside events from multiple aspects. In this paper, we introduce multi-grained contrastive learning and triple-mixture of experts (MCTM) for event representation learning. Our proposed method extends the random MLM by incorporating a specialized MLM designed to capture different grammatical structures within events, which allows the model to learn token-level knowledge from multiple perspectives. Furthermore, we have observed that mask tokens with different granularities affect the model differently, therefore, we incorporate mixture of experts (MoE) to learn importance weights associated with different granularities. Our experiments demonstrate that MCTM outperforms other baselines in tasks such as hard similarity and transitive sentence similarity, highlighting the superiority of our method.

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Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection
Jingyao Tang | Lishuang Li | Hongbin Lu | Xueyang Qin | Beibei Zhang | Haiming Wu
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Few-shot Event Detection (FSED) is a meaningful task due to the limited labeled data and expensive manual labeling. Some prompt-based methods are used in FSED. However, these methods require large GPU memory due to the increased length of input tokens caused by concatenating prompts, as well as additional human effort for designing verbalizers. Moreover, they ignore instance and prompt biases arising from the confounding effects between prompts and texts. In this paper, we propose a prototype-based prompt-instance Interaction with causal Intervention (2xInter) model to conveniently utilize both prompts and verbalizers and effectively eliminate all biases. Specifically, 2xInter first presents a Prototype-based Prompt-Instance Interaction (PPII) module that applies an interactive approach for texts and prompts to reduce memory and regards class prototypes as verbalizers to avoid design costs. Next, 2xInter constructs a Structural Causal Model (SCM) to explain instance and prompt biases and designs a Double-View Causal Intervention (DVCI) module to eliminate these biases. Due to limited supervised information, DVCI devises a generation-based prompt adjustment for instance intervention and a Siamese network-based instance contrasting for prompt intervention. Finally, the experimental results show that 2xInter achieves state-of-the-art performance on RAMS and ACE datasets.


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基于平行交互注意力网络的中文电子病历实体及关系联合抽取(Parallel Interactive Attention Network for Joint Entity and Relation Extraction Based on Chinese Electronic Medical Record)
LiShuang Li (李丽双) | Zehao Wang (王泽昊) | Xueyang Qin (秦雪洋) | Yuan Guanghui (袁光辉)
Proceedings of the 21st Chinese National Conference on Computational Linguistics