Huaiyu Wan


2024

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KPatch: Knowledge Patch to Pre-trained Language Model for Zero-Shot Stance Detection on Social Media
Shuohao Lin | Wei Chen | Yunpeng Gao | Zhishu Jiang | Mengqi Liao | Zhiyu Zhang | Shuyuan Zhao | Huaiyu Wan
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Zero-shot stance detection on social media (ZSSD-SM) aims to distinguish the attitude in tweets towards an unseen target. Previous work capture latent variables between source and target domains to perform this task, but the lack of context knowledge hinders the detection performance. Recent studies have been devoted to obtaining the accurate representation of tweets by bringing additional facts from Knowledge Graph (KG), showing promising performance. However, these knowledge injection methods still suffer from two challenges: (i) The pipeline of knowledge injection causes error accumulation and (ii) irrelevant knowledge makes them fail to understand the semantics. In this paper, we propose a novel knowledge injection method for ZSSD-SM, which adopts two training stages, namely knowledge compression and task guidance, to flexibly inject knowledge into the pre-trained language model (PLM) and adaptively expand tweets context. Specifically, in the knowledge compression stage, the latent representation of KG is reconstructed by the triplet denoising task and compressed into external matrices; while in the task guidance stage, the frozen matrices are employed to guide the PLM to adaptively extract its own context-related knowledge, and then complete the fine-tuning of the ZSSD-SM task. Extensive experiments on multiple datasets show the effectiveness of our proposed method. The code is available at: https://github.com/ShuohaoLin/KPatch.

2023

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Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
Shuhan Wu | Huaiyu Wan | Wei Chen | Yuting Wu | Junfeng Shen | Youfang Lin
Findings of the Association for Computational Linguistics: EMNLP 2023

Graph neural networks (GNNs) have shown promising performance for knowledge graph reasoning. A recent variant of GNN called progressive relational graph neural network (PRGNN), utilizes relational rules to infer missing knowledge in relational digraphs and achieves notable results. However, during reasoning with PRGNN, two important properties are often overlooked: (1) the sequentiality of relation composition, where the order of combining different relations affects the semantics of the relational rules, and (2) the lagged entity information propagation, where the transmission speed of required information lags behind the appearance speed of new entities. Ignoring these properties leads to incorrect relational rule learning and decreased reasoning accuracy. To address these issues, we propose a novel knowledge graph reasoning approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN). Specifically, RUN-GNN employs a query related fusion gate unit to model the sequentiality of relation composition and utilizes a buffering update mechanism to alleviate the negative effect of lagged entity information propagation, resulting in higher-quality relational rule learning. Experimental results on multiple datasets demonstrate the superiority of RUN-GNN is superior on both transductive and inductive link prediction tasks.

2019

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Exploiting the Entity Type Sequence to Benefit Event Detection
Yuze Ji | Youfang Lin | Jianwei Gao | Huaiyu Wan
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Event Detection (ED) is one of the most important task in the field of information extraction. The goal of ED is to find triggers in sentences and classify them into different event types. In previous works, the information of entity types are commonly utilized to benefit event detection. However, the sequential features of entity types have not been well utilized yet in the existing ED methods. In this paper, we propose a novel ED approach which learns sequential features from word sequences and entity type sequences separately, and combines these two types of sequential features with the help of a trigger-entity interaction learning module. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods.