Guodong Zhou

AAAI Conference 2026 Conference Paper

PCoKG: Personality-aware Commonsense Reasoning with Debate

• Weijie Li
• Zhongqing Wang
• Guodong Zhou

Most commonsense reasoning models overlook the influence of personality traits, limiting their effectiveness in personalized systems such as dialogue generation. To address this limitation, we introduce the Personality-aware Commonsense Knowledge Graph (PCoKG), a structured dataset comprising 521,316 quadruples. We begin by employing three evaluators to score and filter events from the ATOMIC dataset, selecting those that are likely to elicit diverse reasoning patterns across different personality types. For knowledge graph construction, we leverage the role-playing capabilities of large language models (LLMs) to perform reasoning tasks. To enhance the quality of the generated knowledge, we incorporate a debate mechanism consisting of a proponent, an opponent, and a judge, which iteratively refines the outputs through feedback loops. We evaluate the dataset from multiple perspectives and conduct fine-tuning and ablation experiments using multiple LLM backbones to assess PCoKG's robustness and the effectiveness of its construction pipeline. Our LoRA-based fine-tuning results indicate a positive correlation between model performance and the parameter scale of the base models. Finally, we apply PCoKG to persona-based dialogue generation, where it demonstrates improved consistency between generated responses and reference outputs. This work bridges the gap between commonsense reasoning and individual cognitive differences, enabling the development of more personalized and context-aware AI systems.

AAAI Conference 2026 Conference Paper

RFKG-CoT: Relation-Driven Adaptive Hop-count Selection and Few-Shot Path Guidance for Knowledge-Aware QA

• Chao Zhang
• Minghan Li
• Tianrui Lv
• Guodong Zhou

Large language models (LLMs) often generate hallucinations in knowledge-intensive QA due to parametric knowledge limitations. While existing methods like KG-CoT improve reliability by integrating knowledge graph (KG) paths, they suffer from rigid hop-count selection (solely question-driven) and underutilization of reasoning paths (lack of guidance). To address this, we propose RFKG-CoT: First, it replaces the rigid hop-count selector with a relation-driven adaptive hop-count selector that dynamically adjusts reasoning steps by activating KG relations (e.g., 1-hop for direct ''brother" relations, 2-hop for indirect ''father-son" chains), formalized via a relation mask. Second, it introduces a few-shot in-context learning path guidance mechanism with CoT (think) that constructs examples in a ''question-paths-answer" format to enhance LLMs' ability to understand reasoning paths. Experiments on four KGQA benchmarks show RFKG-CoT improves accuracy by up to 14.7 pp (Llama2-7B on WebQSP) over KG-CoT. Ablations confirm the hop-count selector and the path prompt are complementary, jointly transforming KG evidence into more faithful answers.

AAAI Conference 2026 Conference Paper

Towards Closed-Loop Embodied Empathy Evolution: Probing LLM-Centric Lifelong Empathic Motion Generation in Unseen Scenarios

• Jiawen Wang
• Jingjing Wang
• Tianyang Chen
• Min Zhang
• Guodong Zhou

In the literature, existing human-centric emotional motion generation methods primarily focus on boosting performance within a single scale-fixed dataset, largely neglecting the flexible and scale-increasing motion scenarios (e.g., sports, dance), whereas effectively learning these newly emerging scenarios can significantly enhance the model’s real-world generalization ability. Inspired by this, this paper proposes a new LLM-Centric Lifelong Empathic Motion Generation (L2-EMG) task, which aims to equip LLMs with the capability to continually acquire emotional motion generation knowledge across different unseen scenarios, potentially contributing to building a closed-loop and self-evolving embodied agent equipped with both empathy and intelligence. Further, this paper poses two key challenges in the L2-EMG task, i.e., the emotion decoupling challenge and the scenario adapting challenge. To this end, this paper proposes an Emotion-Transferable and Scenario-Adapted Mixture of Experts (ES-MoE) approach which designs a causal-guided emotion decoupling block and a scenario-adapted expert constructing block to address the two challenges, respectively. Especially, this paper constructs multiple L2-EMG datasets to validate the effectiveness of the ES-MoE approach. Extensive evaluations show that ES-MoE outperforms advanced baselines.

JAIR Journal 2025 Journal Article

MRC and Transfer Learning Framework for Document-level Event Factuality Identification with Heterogeneous Spectral Attention Networks

• Zhong Qian
• Peifeng Li
• Qiaoming Zhu
• Guodong Zhou

This paper concentrates on Document-level Event Factuality Identification (DEFI) that predicts event factuality values from the viewpoint of the document. At present, the shortcomings of previous studies are multi-fold, including data limitation and scarcity, coarsegrained interpretability without span-level factuality clues, no unified model for different datasets. This paper is devoted to address the above problems by building unified Machine Reading Comprehension (MRC) frameworks comprised of both span-extraction and multiple-choice styles, which exploit Heterogeneous Spectral Attention Networks (HSAN) with spectral networks and hypergraph attention networks as the fine-grained encoders, especially for span-level encoding. Moreover, we integrate Transfer Learning (TL) as cross-domain data augmentation to learn more span-level information from classical MRC datasets by source and target adapters. Experimental performance on ExDLEF corpus, which contains both English and Chinese documents, shows that our span-extraction MRC model is superior to several state-of-the-art baselines, and proves the effectiveness of transfer learning under MRC paradigms.

EAAI Journal 2024 Journal Article

Speculation and negation identification via unified Machine Reading Comprehension frameworks with lexical and syntactic data augmentation

• Zhong Qian
• Ting Zou
• Zihao Zhang
• Peifeng Li
• Qiaoming Zhu
• Guodong Zhou

Speculation and Negation Identification focuses on the extraction of speculative and negative cues and scopes. Previous work relied on complete syntactic trees or simply fed sentences into pre-trained language models, which were confronted with poor generalization within and across datasets, and the limitations of training samples. Accordingly, we build a complete pipeline framework that firstly detects cues and then extracts scopes, and propose unified Machine Reading Comprehension paradigms for both cue detection and scope resolution on several datasets. To tackle the insufficiency of training sets and produce more useful samples with appropriate amount of lexical and syntactic knowledge, we apply data augmentation integrating lexical and syntactic features for scope resolution. Experimental results show that our model achieves higher performance than baselines on several publicly accessible corpora.

ECAI Conference 2023 Conference Paper

Comment-Aware Multi-Modal Heterogeneous Pre-Training for Humor Detection in Short-Form Videos

• Yang Liu 0358
• Huanqin Ping
• Dong Zhang 0013
• Qingying Sun
• Shoushan Li
• Guodong Zhou

Conventional humor analysis normally focuses on text, text-image pair, and even long video (e. g. , monologue) scenarios. However, with the recent rise of short-form video sharing, humor detection in this scenario has not yet gained much exploration. To the best of our knowledge, there are two primary issues associated with short-form video humor detection (SVHD): 1) At present, there are no ready-made humor annotation samples in this scenario, and it takes a lot of manpower and material resources to obtain a large number of annotation samples; 2) Unlike the more typical audio and visual modalities, the titles (as opposed to simultaneous transcription in the lengthy film) and associated interactive comments in short-form videos may convey apparent humorous clues. Therefore, in this paper, we first collect and annotate a video dataset from DouYin (aka. TikTok in the world), namely DY24h, with hierarchical comments. Then, we also design a novel approach with comment-aided multi-modal heterogeneous pre-training (CMHP) to introduce comment modality in SVHD. Extensive experiments and analysis demonstrate that our CMHP beats several existing video-based approaches on DY24h, and that the comments modality further aids a better comprehension of humor. Our dataset, code and pre-trained models are available at https: //github. com/yliu-cs/CMHP.

IJCAI Conference 2022 Conference Paper

Document-level Event Factuality Identification via Reinforced Multi-Granularity Hierarchical Attention Networks

• Zhong Qian
• Peifeng Li
• Qiaoming Zhu
• Guodong Zhou

Document-level Event Factuality Identification (DEFI) predicts the event factuality according to the current document, and mainly depends on event-related tokens and sentences. However, previous studies relied on annotated information and did not filter irrelevant and noisy texts. Therefore, this paper proposes a novel end-to-end model, i. e. , Reinforced Multi-Granularity Hierarchical Attention Network (RMHAN), which can learn information at different levels of granularity from tokens and sentences hierarchically. Moreover, with hierarchical reinforcement learning, RMHAN first selects relevant and meaningful tokens, and then selects useful sentences for document-level encoding. Experimental results on DLEF-v2 corpus show that RMHAN model outperforms several state-of-the-art baselines and achieves the best performance.

IJCAI Conference 2022 Conference Paper

Towards Discourse-Aware Document-Level Neural Machine Translation

• Xin Tan
• Longyin Zhang
• Fang Kong
• Guodong Zhou

Current document-level neural machine translation (NMT) systems have achieved remarkable progress with document context. Nevertheless, discourse information that has been proven effective in many NLP tasks is ignored in most previous work. In this work, we aim at incorporating the coherence information hidden within the RST-style discourse structure into machine translation. To achieve it, we propose a document-level NMT system enhanced with the discourse-aware document context, which is named Disco2NMT. Specifically, Disco2NMT models document context based on the discourse dependency structures through a hierarchical architecture. We first convert the RST tree of an article into a dependency structure and then build the graph convolutional network (GCN) upon the segmented EDUs under the guidance of RST dependencies to capture the discourse-aware context for NMT incorporation. We conduct experiments on the document-level English-German and English-Chinese translation tasks with three domains (TED, News, and Europarl). Experimental results show that our Disco2NMT model significantly surpasses both context-agnostic and context-aware baseline systems on multiple evaluation indicators.

IJCAI Conference 2021 Conference Paper

Improving Context-Aware Neural Machine Translation with Source-side Monolingual Documents

• Linqing Chen
• Junhui Li
• Zhengxian Gong
• Xiangyu Duan
• Boxing Chen
• Weihua Luo
• Min Zhang
• Guodong Zhou

Document context-aware machine translation remains challenging due to the lack of large-scale document parallel corpora. To make full use of source-side monolingual documents for context-aware NMT, we propose a Pre-training approach with Global Context (PGC). In particular, we first propose a novel self-supervised pre-training task, which contains two training objectives: (1) reconstructing the original sentence from a corrupted version; (2) generating a gap sentence from its left and right neighbouring sentences. Then we design a universal model for PGC which consists of a global context encoder, a sentence encoder and a decoder, with similar architecture to typical context-aware NMT models. We evaluate the effectiveness and generality of our pre-trained PGC model by adapting it to various downstream context-aware NMT models. Detailed experimentation on four different translation tasks demonstrates that our PGC approach significantly improves the translation performance of context-aware NMT. For example, based on the state-of-the-art SAN model, we achieve an averaged improvement of 1. 85 BLEU scores and 1. 59 Meteor scores on the four translation tasks.

AAAI Conference 2021 Conference Paper

Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance

• Dong Zhang
• Suzhong Wei
• Shoushan Li
• Hanqian Wu
• Qiaoming Zhu
• Guodong Zhou

Multi-modal named entity recognition (MNER) aims to discover named entities in free text and classify them into predefined types with images. However, dominant MNER models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have the potential to refine multi-modal representation learning. To deal with this issue, we propose a unified multi-modal graph fusion (UMGF) approach for MNER. Specifically, we first represent the input sentence and image using a unified multi-modal graph, which captures various semantic relationships between multi-modal semantic units (words and visual objects). Then, we stack multiple graph-based multi-modal fusion layers that iteratively perform semantic interactions to learn node representations. Finally, we achieve an attentionbased multi-modal representation for each word and perform entity labeling with a CRF decoder. Experimentation on the two benchmark datasets demonstrates the superiority of our MNER model.

AAAI Conference 2021 Conference Paper

Multi-modal Multi-label Emotion Recognition with Heterogeneous Hierarchical Message Passing

• Dong Zhang
• Xincheng Ju
• Wei Zhang
• Junhui Li
• Shoushan Li
• Qiaoming Zhu
• Guodong Zhou

As an important research issue in affective computing community, multi-modal emotion recognition has become a hot topic in the last few years. However, almost all existing studies perform multiple binary classification for each emotion with focus on complete time series data. In this paper, we focus on multi-modal emotion recognition in a multilabel scenario. In this scenario, we consider not only the label-to-label dependency, but also the feature-to-label and modality-to-label dependencies. Particularly, we propose a heterogeneous hierarchical message passing network to effectively model above dependencies. Furthermore, we propose a new multi-modal multi-label emotion dataset based on partial time-series content to show predominant generalization of our model. Detailed evaluation demonstrates the effectiveness of our approach.

AAAI Conference 2020 Conference Paper

Sentiment Classification in Customer Service Dialogue with Topic-Aware Multi-Task Learning

• Jiancheng Wang
• Jingjing Wang
• Changlong Sun
• Shoushan Li
• Xiaozhong Liu
• Luo Si
• Min Zhang
• Guodong Zhou

Sentiment analysis in dialogues plays a critical role in dialogue data analysis. However, previous studies on sentiment classification in dialogues largely ignore topic information, which is important for capturing overall information in some types of dialogues. In this study, we focus on the sentiment classification task in an important type of dialogue, namely customer service dialogue, and propose a novel approach which captures overall information to enhance the classification performance. Specifically, we propose a topic-aware multi-task learning (TML) approach which learns topicenriched utterance representations in customer service dialogue by capturing various kinds of topic information. In the experiment, we propose a large-scale and high-quality annotated corpus for the sentiment classification task in customer service dialogue and empirical studies on the proposed corpus show that our approach significantly outperforms several strong baselines.

IJCAI Conference 2019 Conference Paper

Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations

• Dong Zhang
• Liangqing Wu
• Changlong Sun
• Shoushan Li
• Qiaoming Zhu
• Guodong Zhou

Recently, emotion detection in conversations becomes a hot research topic in the Natural Language Processing community. In this paper, we focus on emotion detection in multi-speaker conversations instead of traditional two-speaker conversations in existing studies. Different from non-conversation text, emotion detection in conversation text has one specific challenge in modeling the context-sensitive dependence. Besides, emotion detection in multi-speaker conversations endorses another specific challenge in modeling the speaker-sensitive dependence. To address above two challenges, we propose a conversational graph-based convolutional neural network. On the one hand, our approach represents each utterance and each speaker as a node. On the other hand, the context-sensitive dependence is represented by an undirected edge between two utterances nodes from the same conversation and the speaker-sensitive dependence is represented by an undirected edge between an utterance node and its speaker node. In this way, the entire conversational corpus can be symbolized as a large heterogeneous graph and the emotion detection task can be recast as a classification problem of the utterance nodes in the graph. The experimental results on a multi-modal and multi-speaker conversation corpus demonstrate the great effectiveness of the proposed approach.

IJCAI Conference 2018 Conference Paper

Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks

• Jingjing Wang
• Jie Li
• Shoushan Li
• Yangyang Kang
• Min Zhang
• Luo Si
• Guodong Zhou

Aspect sentiment classification, a challenging task in sentiment analysis, has been attracting more and more attention in recent years. In this paper, we highlight the need for incorporating the importance degrees of both words and clauses inside a sentence and propose a hierarchical network with both word-level and clause-level attentions to aspect sentiment classification. Specifically, we first adopt sentence-level discourse segmentation to segment a sentence into several clauses. Then, we leverage multiple Bi-directional LSTM layers to encode all clauses and propose a word-level attention layer to capture the importance degrees of words in each clause. Third and finally, we leverage another Bi-directional LSTM layer to encode the outputs from the former layers and propose a clause-level attention layer to capture the importance degrees of all the clauses inside a sentence. Experimental results on the laptop and restaurant datasets from SemEval-2015 demonstrate the effectiveness of our proposed approach to aspect sentiment classification.

IJCAI Conference 2018 Conference Paper

Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification

• Zhong Qian
• Peifeng Li
• Yue Zhang
• Guodong Zhou
• Qiaoming Zhu

Event factuality identification is an important semantic task in NLP. Traditional research heavily relies on annotated texts. This paper proposes a two-step framework, first extracting essential factors related with event factuality from raw texts as the input, and then identifying the factuality of events via a Generative Adversarial Network with Auxiliary Classification (AC-GAN). The use of AC-GAN allows the model to learn more syntactic information and address the imbalance among factuality values. Experimental results on FactBank show that our method significantly outperforms several state-of-the-art baselines, particularly on events with embedded sources, speculative and negative factuality values.

IJCAI Conference 2017 Conference Paper

Joint Learning on Relevant User Attributes in Micro-blog

• Jingjing Wang
• Shoushan Li
• Guodong Zhou

User attribute classification aims to identify users’ attributes (e. g. , gender, age and profession) by leveraging user generated content. However, conventional approaches to user attribute classification focus on single attribute classification involving only one user attribute, which completely ignores the relationship among various user attributes. In this paper, we confront a novel scenario in user attribute classification where relevant user attributes are jointly learned, attempting to make the relevant attribute classification tasks help each other. Specifically, we propose a joint learning approach, namely Aux-LSTM, which first learns a proper auxiliary representation between the related tasks and then leverages the auxiliary representation to integrate the learning process in both tasks. Empirical studies demonstrate the effectiveness of our proposed approach to joint learning on relevant user attributes.

IJCAI Conference 2015 Conference Paper

Interactive Gender Inference with Integer Linear Programming

• Shoushan Li
• Jingjing Wang
• Guodong Zhou
• Hanxiao Shi

IJCAI Conference 2013 Conference Paper

A Clause-Level Hybrid Approach to Chinese Empty Element Recovery

• Fang Kong
• Guodong Zhou

Empty elements (EEs) play a critical role in Chinese syntactic, semantic and discourse analysis. Previous studies employ a language-independent sentence-level approach to EE recovery, by casting it as a linear tagging or structured parsing problem. In comparison, this paper proposes a clauselevel hybrid approach to address specific problems in Chinese EE recovery, which recovers EEs in Chinese language from the clause perspective and integrates the advantages of both linear tagging and structured parsing. In particular, a comma disambiguation method is employed to improve syntactic parsing and help determine clauses in Chinese. In this way, the noise introduced by sentencelevel syntactic parsing and multiple EEs in the same position of a linear sentence can be well addressed. Evaluation on Chinese Treebank 6. 0 shows the significant performance improvement of our clause-level hybrid approach over the state-ofthe-art sentence-level baselines, and its great impact on a state-of-the-art Chinese syntactic parser.

IJCAI Conference 2013 Conference Paper

Active Learning for Cross-Domain Sentiment Classification

• Shoushan Li
• Yunxia Xue
• Zhongqing Wang
• Guodong Zhou

In the literature, various approaches have been proposed to address the domain adaptation problem in sentiment classification (also called cross-domain sentiment classification). However, the adaptation performance normally much suffers when the data distributions in the source and target domains differ significantly. In this paper, we suggest to perform active learning for cross-domain sentiment classification by actively selecting a small amount of labeled data in the target domain. Accordingly, we propose an novel active learning approach for cross-domain sentiment classification. First, we train two individual classifiers, i. e. , the source and target classifiers with the labeled data from the source and target respectively. Then, the two classifiers are employed to select informative samples with the selection strategy of Query By Committee (QBC). Third, the two classifier is combined to make the classification decision. Importantly, the two classifiers are trained by fully exploiting the unlabeled data in the target domain with the label propagation (LP) algorithm. Empirical studies demonstrate the effectiveness of our active learning approach for cross-domain sentiment classification over some strong baselines. 1

IJCAI Conference 2013 Conference Paper

Joint Modeling of Argument Identification and Role Determination in Chinese Event Extraction with Discourse-Level Information

• Peifeng Li
• Qiaoming Zhu
• Guodong Zhou

Argument extraction is a challenging task in event extraction. However, most of previous studies focused on intra-sentence information and failed to extract inter-sentence arguments. This paper proposes a discourse-level joint model of argument identification and role determination to infer those inter-sentence arguments in a discourse. Moreover, to better represent the relationship among relevant event mentions and the relationship between an event mention and its arguments in a discourse, this paper introduces various kinds of corpus-based and discourse-based constraints in the joint model, either automatically learned or linguistically motivated. Evaluation on the ACE 2005 Chinese corpus justifies the effectiveness of our joint model over a strong baseline in Chinese argument extraction, in particular argument identification.

AAAI Conference 2012 Conference Paper

Opinion Target Extraction Using a Shallow Semantic Parsing Framework

• Shoushan Li
• Rongyang Wang
• Guodong Zhou

IJCAI Conference 2011 Conference Paper

Improve Tree Kernel-Based Event Pronoun Resolution with Competitive Information

• Fang Kong
• Guodong Zhou

Event anaphora resolution plays a critical role in discourse analysis. This paper proposes a tree kernel-based framework for event pronoun resolution. In particular, a new tree expansion scheme is introduced to automatically determine a proper parse tree structure for event pronoun resolution by considering various kinds of competitive information related with the anaphor and the antecedent candidate. Evaluation on the OntoNotes English corpus shows the appropriateness of the tree kernel-based framework and the effectiveness of competitive information for event pronoun resolution.

IJCAI Conference 2011 Conference Paper

Semi-Supervised Learning for Imbalanced Sentiment Classification

• Shoushan Li
• Zhongqing Wang
• Guodong Zhou
• Sophia Yat Mei Lee

Various semi-supervised learning methods have been proposed recently to solve the long-standing shortage problem of manually labeled data in sentiment classification. However, most existing studies assume the balance between negative and positive samples in both the labeled and unlabeled data, which may not be true in reality. In this paper, we investigate a more common case of semi-supervised learning for imbalanced sentiment classification. In particular, various random subspaces are dynamically generated to deal with the imbalanced class distribution problem. Evaluation across four domains shows the effectiveness of our approach.