Jian Liao


2023

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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

“随着社交媒体的快速发展,多模态数据呈爆炸性增长,如何从多模态数据中挖掘和理解情感信息,已经成为一个较为热门的研究方向。而现有的基于文本、视频和音频的多模态情感分析方法往往将不同模态的高级特征与低级特征进行融合,忽视了不同模态特征层次之间的差异。因此,本文采用以文本模态为中心,音频模态和视频模态为补充的方式,提出了多任务多模态交互学习的自监督动态融合模型。通过多层的结构,构建了单模态特征表示与两两模态特征的层次融合表示,使模型将不同层次的特征进行融合,并设计了从低级特征渐变到高级特征的融合策略。为了进一步加强多模态特征融合,使用了分布相似性损失函数和异质损失函数,用于学习模态的共性表征和特性表征。在此基础上,利用多任务学习,获得模态的一致性及差异性特征。通过在CMU-MOSI和CMU-MOSEI数据集上分别实验,实验结果表明本文模型的情感分类性能优于基线模型。”

2022

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基于异构用户知识融合的隐式情感分析研究(Research on Implicit Sentiment Analysis based on Heterogeneous User Knowledge Fusion)
Jian Liao (廖健) | Kai Zhang (张楷) | Suge Wang (王素格) | Jia Lei (雷佳) | Yiyang Zhang (张益阳)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“隐式情感分析因其缺乏显式情感线索的特性是情感分析领域的重要研究难点之一。传统的隐式情感分析方法通常针对隐式情感文本本身的信息进行建模,没有考虑隐式情感的主观差异性特征。本文提出了一种基于异构用户知识融合的隐式情感分析模型HELENE,首先从用户数据中挖掘用户异构的内容知识、社会化属性知识以及社会化关系知识,异构用户知识融合学习框架基于图神经网络模型结合动态预训练模型分别从用户的内部信息和外部信息两个维度对其进行画像建模;在此基础上与隐式情感文本语义信息进行融合学习,使得模型可以对隐式情感进行主观差异化建模表示。此外,本文构建了一个用户个性化通用情感分析语料库,涵盖了较为完整的文本内容信息、用户社会化属性信息和关系信息,可同时满足面向用户个性化建模的隐式或显式情感分析相关研究任务的需要。在所构建数据集上的实验结果显示,本文的方法相比基线模型在用户个性化隐式情感分析任务上具有显著的提升效果。”

2021

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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.

2020

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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.