Jianxing Zheng

Also published as: JianXing Zheng


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Enhancing Event Causality Identification with Event Causal Label and Event Pair Interaction Graph
Ruili Pu | Yang Li | Suge Wang | Deyu Li | Jianxing Zheng | Jian Liao
Findings of the Association for Computational Linguistics: ACL 2023

Most existing event causality identification (ECI) methods rarely consider the event causal label information and the interaction information between event pairs. In this paper, we propose a framework to enrich the representation of event pairs by introducing the event causal label information and the event pair interaction information. In particular, 1) we design an event-causal-label-aware module to model the event causal label information, in which we design the event causal label prediction task as an auxiliary task of ECI, aiming to predict which events are involved in the causal relationship (we call them causality-related events) by mining the dependencies between events. 2) We further design an event pair interaction graph module to model the interaction information between event pairs, in which we construct the interaction graph with event pairs as nodes and leverage graph attention mechanism to model the degree of dependency between event pairs. The experimental results show that our approach outperforms previous state-of-the-art methods on two benchmark datasets EventStoryLine and Causal-TimeBank.

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Hierarchical Enhancement Framework for Aspect-based Argument Mining
Yujie Fu | Yang Li | Suge Wang | Xiaoli Li | Deyu Li | Jian Liao | JianXing Zheng
Findings of the Association for Computational Linguistics: EMNLP 2023

Aspect-Based Argument Mining (ABAM) is a critical task in computational argumentation. Existing methods have primarily treated ABAM as a nested named entity recognition problem, overlooking the need for tailored strategies to effectively address the specific challenges of ABAM tasks. To this end, we propose a layer-based Hierarchical Enhancement Framework (HEF) for ABAM, and introduce three novel components: the Semantic and Syntactic Fusion (SSF) component, the Batch-level Heterogeneous Graph Attention Network (BHGAT) component, and the Span Mask Interactive Attention (SMIA) component. These components serve the purposes of optimizing underlying representations, detecting argument unit stances, and constraining aspect term recognition boundaries, respectively. By incorporating these components, our framework enables better handling of the challenges and improves the performance and accuracy in argument unit and aspect term recognition. Experiments on multiple datasets and various tasks verify the effectiveness of the proposed framework and components.

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基于多任务多模态交互学习的情感分类方法(Sentiment classification method based on multitasking and multimodal interactive learning)
Peng Xue (薛鹏) | Yang Li (李旸) | Suge Wang (王素格) | Jian Liao (廖健) | Jianxing Zheng (郑建兴) | Yujie Fu (符玉杰) | Deyu Li (李德玉)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics



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Emotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy
Dayu Li | Xiaodan Zhu | Yang Li | Suge Wang | Deyu Li | Jian Liao | Jianxing Zheng
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Emotion inference in multi-turn conversations aims to predict the participant’s emotion in the next upcoming turn without knowing the participant’s response yet, and is a necessary step for applications such as dialogue planning. However, it is a severe challenge to perceive and reason about the future feelings of participants, due to the lack of utterance information from the future. Moreover, it is crucial for emotion inference to capture the characteristics of emotional propagation in conversations, such as persistence and contagiousness. In this study, we focus on investigating the task of emotion inference in multi-turn conversations by modeling the propagation of emotional states among participants in the conversation history, and propose an addressee-aware module to automatically learn whether the participant keeps the historical emotional state or is affected by others in the next upcoming turn. In addition, we propose an ensemble strategy to further enhance the model performance. Empirical studies on three different benchmark conversation datasets demonstrate the effectiveness of the proposed model over several strong baselines.


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Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder
Wenyue Zhang | Xiaoli Li | Yang Li | Suge Wang | Deyu Li | Jian Liao | Jianxing Zheng
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Detecting public sentiment drift is a challenging task due to sentiment change over time. Existing methods first build a classification model using historical data and subsequently detect drift if the model performs much worse on new data. In this paper, we focus on distribution learning by proposing a novel Hierarchical Variational Auto-Encoder (HVAE) model to learn better distribution representation, and design a new drift measure to directly evaluate distribution changes between historical data and new data. Our experimental results demonstrate that our proposed model achieves better results than three existing state-of-the-art methods.