The subtlety of emotional expressions makes implicit emotion analysis (IEA) particularly sensitive to user-specific characteristics. Current studies personalize emotion analysis by focusing on the author but neglect the impact of the intended reader on implicit emotional feedback. In this paper, we introduce Personalized IEA (PIEA) and present the RAPPIE model, which addresses subjective variability by incorporating reader feedback. In particular, (1) we create reader agents based on large language models to simulate reader feedback, overcoming the issue of “spiral of silence effect” and data incompleteness of real reader reaction. (2) We develop a role-aware multi-view graph learning to model the emotion interactive propagation process in scenarios with sparse reader information. (3) We construct two new PIEA datasets covering English and Chinese social media with detailed user metadata, addressing the text-centric limitation of existing datasets. Extensive experiments show that RAPPIE significantly outperforms state-of-the-art baselines, demonstrating the value of incorporating reader feedback in PIEA.

Joint relation extraction models effectively mitigate the error propagation problem inherently present in pipeline models. Nevertheless, joint models face challenges including high computational complexity, complex network architectures, difficult parameter tuning, and notably, limited interpretability. In contrast, recent advances in pipeline relation extraction models (PURE, PL-Marker) have attracted considerable attention due to their lightweight design and high extraction accuracy. A key advancement is the introduction of a marker mechanism, which enhances relation extraction (RE) process by highlighting entities. However, these models primarily focus on generating correct labels. In doing so, they neglect the label selection process. Moreover, they fail to adequately capture the intricate interactions between entity pairs. To overcome these limitations, we develop a Candidate Label Markers (CLMs) mechanism that prioritizes strategic label selection over simple label generation. Furthermore, we facilitate interactions among diverse relation pairs, enabling the identification of more intricate relational patterns. Experimental results show that we achieve a new SOTA performance. Specifically, based on the same Named Entity Recognition (NER) results as theirs, we improve the SOTA methods by 2.5%, 1.9%, 1.2% in terms of strict F1 scores on SciERC, ACE05 and ACE04.

“文档级事件抽取是自然语言处理中的一项重要任务,面临论元分散和多事件提及的挑战,现有研究通常从文档的所有句子中抽取论元,通过论元角色建模捕获实体间关系,忽略了文档中事件-句子间的关联差异性。本文提出了一种融合扩展语义和标签层次信息的文档级事件抽取方法。首先,利用大语言模型对文本和事件类型标签与论元角色标签进行语义扩展,以引入更丰富的背景语义信息;其次,基于关联差异性的事件类型检测模块,获取文档中与事件类型高度相关的句子,通过约束候选实体的抽取范围,来缓解论元分散问题;进一步,针对文档提及的多个事件类型,利用有向无环图从候选实体中抽取论元,获取所有事件要素。在ChFinAnn和DuEE-Fin两个数据集上的实验结果表明,本文提出的方法相比基线模型可以有针对性地缓解多个事件所属论元分散的问题,有效地提升事件抽取的性能。”

“在散文中,作者的情绪会伴随着文章的段落或者句子发生变化,比如从悲伤到快乐、从喜悦到愤怒。为此,本文构建散文情绪变化数据集,提出一种基于双图注意力网络的多种知识融合的情绪变化分析方法。首先,引入意象知识库,建立融合意象知识的句子表示;其次,构建上下文带权依赖图和语篇带权依赖图,通过融合上下文知识和语篇结构,建立了融合上下文知识、语篇结构的句子表示;同时设计愉悦效价识别层,获得融合愉悦效价信息的句子表示;在此基础上,将以上三者表示进行拼接,通过全连接网络得到最终的情绪变化结果。实验结果表明,本文提出的方法可以有效识别情绪变化,为散文阅读理解中的思想情绪变化类问题的解答提供帮助。”

“在散文阅读理解中,一方面问题的题干通常较为简洁、用词较为抽象,机器难以直接理解问题的含义和要求;另一方面,散文文章较长,答案候选句分散在文章的多个段落,给答案候选句的抽取任务带来巨大的挑战。因此,本文提出了一种基于问题扩展的散文答案候选句抽取方法。首先,利用大语言模型抽取文章中与问题题干相关的词,构建问题词扩展库,其次,利用大语言模型强大的生成能力对原问题的题干进行重写,进一步,利用问题词扩展库对其扩展,最后,通过对散文文章分块处理,建立基于全局上下文信息、历史信息的问题和文章句子的相关性判断模型,用于抽取答案候选句。通过在散文阅读理解数据集上进行实验,实验结果表明本文提出的方法提高了散文抽取答案候选句的准确率,为散文阅读理解的生成类问题的解答提供了技术支撑。”

Document-level Event Extraction (DEE) is a vital task in NLP as it seeks to automatically recognize and extract event information from a document. However, current approaches often overlook intricate relationships among events and subtle correlations among arguments within a document, which can significantly impact the effectiveness of event type recognition and the extraction of cross-sentence arguments in DEE task. This paper proposes a novel Correlation Association Interactive Network (CAINet), comprising two key components: event relationship graph and argument correlation graph. In particular, the event relationship graph models the relationship among various events through structural associations among event nodes and sentence nodes, to improve the accuracy of event recognition. On the other hand, the arguments correlation graph models the correlations among arguments by quantifying the strength of association among arguments, to effectively aggregate cross-sentence arguments, contributing to the overall success of DEE. Furthermore, we use the large language model to execute DEE task experiments. Experimental results show the proposed CAINet outperforms existing state-of-the-art models and large language models in terms of F1-score across two benchmark datasets.

This paper describes the SIGHAN-2024 shared task for Chinese dimensional aspect-based sentiment analysis (ABSA), including task description, data preparation, performance metrics, and evaluation results. Compared to representing affective states as several discrete classes (i.e., sentiment polarity), the dimensional approach represents affective states as continuous numerical values (called sentiment intensity) in the valence-arousal space, providing more fine-grained affective states. Therefore, we organized a dimensional ABSA (shorted dimABSA) shared task, comprising three subtasks: 1) intensity prediction, 2) triplet extraction, and 3) quadruple extraction, receiving a total of 214 submissions from 61 registered participants during evaluation phase. A total of eleven teams provided selected submissions for each subtask and seven teams submitted technical reports for the subtasks. This shared task demonstrates current NLP techniques for dealing with Chinese dimensional ABSA. All data sets with gold standards and evaluation scripts used in this shared task are publicly available for future research.

“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。”

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.

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.

“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。”

知识图谱问题生成任务是从给定的知识图谱中生成与其相关的问题。目前,知识图谱问题生成模型主要使用基于RNN或Transformer对知识图谱子图进行编码,但这种方式丢失了显式的图结构化信息,在解码器中忽视了局部信息对节点的重要性。本文提出迭代信息传递图编码器来编码子图,获取子图显式的图结构化信息,此外,我们还使用滑动窗口注意力机制提高RNN解码器,提升子图局部信息对节点的重要度。从WQ和PQ数据集上的实验结果看,我们提出的模型比KTG模型在BLEU4指标上分别高出2.16和15.44,证明了该模型的有效性。

在散文阅读理解的鉴赏类问题中,对拟人句赏析考查比较频繁。目前,已有的工作仅对拟人句中的本体要素进行识别并抽取,存在要素抽取不完整的问题,尤其是当句子中出现多个本体时,需要确定拟人词与各个本体的对应关系。为解决这些问题,本文提出了基于人物特征增强的拟人句要素抽取方法。该方法利用特定领域的特征,增强句子的向量表示,再利用条件随机场模型对拟人句中的本体和拟人词要素进行识别。在此基础上,利用自注意力机制对要素之间的关系进行检测,使用要素同步机制和关系同步机制进行信息交互,用于要素识别和关系检测的输入更新。在自建的拟人数据集上进行<本体,拟人词>抽取的比较实验,结果表明本文提出的模型性能优于其他比较模型。

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.

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.