Papers by Kaisong Song
Low-Resource Comparative Opinion Quintuple Extraction by Data Augmentation with Prompting (2023.findings-emnlp)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to predict comparative opinion quintuples from comparative sentences. |
| Approach: | They propose a low-resource approach to extract comparative opinion quintuples from comparative sentences . they propose augmentation using ChatGPT and a data-centric approach . |
| Outcome: | The proposed approach improves the existing pipeline-based method and achieves state-of-the-art results. |
STICKERCONV: Generating Multimodal Empathetic Responses from Scratch (2024.acl-long)
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Yiqun Zhang, Fanheng Kong, Peidong Wang, Shuang Sun, SWangLing SWangLing, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song
| Challenge: | Prior studies on stickers focused on sentiment analysis and recommendation systems, overlooking their vast potential in empathetic response generation. |
| Approach: | They propose a multimodal empathetic dialogue dataset, STICKERCONV, which simulates human behavior with stickers, and propose evaluative metrics based on LLM. |
| Outcome: | The proposed framework generates contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging e-dialog systems. |
SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)
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YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze
| Challenge: | Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues . |
| Approach: | They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance . |
| Outcome: | The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness. |
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (D18-1)
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| Challenge: | Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. |
| Approach: | They propose a co-attention neural network model for emotion cause analysis with emotional context awareness. |
| Outcome: | The proposed model outperforms the state-of-the-art methods. |
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning (D19-1)
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Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang
| Challenge: | Existing studies fail to provide comprehensive service satisfaction analysis . Existing models fail to include satisfaction polarity classification and sentimental utterance identification . |
| Approach: | They propose a model that predicts customer sentiments and aggregates them into service satisfaction polarity. |
| Outcome: | The proposed model predicts customer sentiments and aggregates them into service satisfaction polarity and reasoning clues. |
Knowledge-Aware Co-Reasoning for Multidisciplinary Collaboration (2025.emnlp-main)
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| Challenge: | Existing multi-agent paradigms rely on prompt engineering and lack of knowledge integration. |
| Approach: | They propose a framework that integrates structured knowledge reasoning into multidisciplinary collaboration by using clinical knowledge graphs to guide dynamic discipline determination. |
| Outcome: | Extensive experiments on academic and real-world datasets demonstrate the effectiveness of the proposed framework. |
Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration (2024.findings-emnlp)
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Weikang Yuan, Junjie Cao, Zhuoren Jiang, Yangyang Kang, Jun Lin, Kaisong Song, Tianqianjin Lin, Pengwei Yan, Changlong Sun, Xiaozhong Liu
| Challenge: | Existing studies have found that when LLMs are given criminal facts and legal rules, then asked whether cases constitute a certain charge, they struggle to understand legal theories and perform basic legal reasoning tasks. |
| Approach: | They propose a task to assess LLMs' understanding of legal theories and reasoning capabilities by using a novel framework: Multi-Agent framework for improving complex legal reasoning capability. |
| Outcome: | The proposed framework improves LLMs' understanding of legal theories and reasoning abilities in real-world scenarios. |
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue (2020.findings-emnlp)
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WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, Luo Si
| Challenge: | Existing research on customer service dialogue generation generates generic responses from sellers . however, such cost prohibits small businesses, and multiturn dialogue generation is becoming more popular. |
| Approach: | They propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information to generate generic seller responses. |
| Outcome: | The proposed model can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset. |
STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion (2023.emnlp-main)
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Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, Xiaozhong Liu
| Challenge: | Commercial news provides rich semantics and timely information for automated financial risk detection. |
| Approach: | They propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph to endorse the risk detection enhancement. |
| Outcome: | The proposed model outperforms existing models in terms of generalization and semantics and annotation. |
PDAMeta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | Existing text data augmentation methods can not ensure the diversity and quality of the generated data, which leads to sub-optimal performance. |
| Approach: | They propose a meta-learning framework with progressive data augmentation for few-shot text classification using prompt-based data augmented by attention-based methods. |
| Outcome: | The proposed framework outperforms state-of-the-art models and shows better robustness on four public few-shot text classification datasets. |
LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation (2026.acl-long)
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Weikang Yuan, Kaisong Song, Zhuoren Jiang, Junjie Cao, Yujie Zhang, Jun Lin, Kun Kuang, Ji Zhang, Xiaozhong Liu
| Challenge: | Current systems for legal consultation are insufficient to handle the knowledge-intensive nature of real-world consultations. |
| Approach: | They propose a multi-turn benchmark dataset to evaluate LLMs in legal consultation settings. |
| Outcome: | The proposed framework assesses LLMs’ consultation capabilities in terms of (1) clarification capability and (2) professional advice quality. |
COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text. |
| Approach: | They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text. |
| Outcome: | The proposed method improves performance on three benchmarks. |
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis (2021.emnlp-main)
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Jiawei Liu, Kaisong Song, Yangyang Kang, Guoxiu He, Zhuoren Jiang, Changlong Sun, Wei Lu, Xiaozhong Liu
| Challenge: | Recent efforts to predict chatbot failure hatches vital apprehensions due to complexity of human conversation. |
| Approach: | They propose a model that integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. |
| Outcome: | The proposed model integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. |
Language Models as Continuous Self-Evolving Data Engineers (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their further evolution is often hampered by the scarcity of high-quality training data and the heavy reliance of traditional methods on expert-labeled data. |
| Approach: | They propose a paradigm that enables LLMs to train themselves by generating, cleaning, reviewing and annotating data with preference information. |
| Outcome: | The proposed model can generate, clean, review, and annotate data with preference information significantly reducing time and cost of post-training data construction. |
SAFO: Stable Adaptive Fairness Optimization for LLM-Based Social Survey Simulation (2026.acl-long)
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| Challenge: | Social survey simulations are increasingly used to improve minority performance and social-welfare metrics. |
| Approach: | They propose a dynamic utility–fairness optimization framework for LLM-based survey simulation that explicitly targets fairness and training stability. |
| Outcome: | The proposed framework improves minority performance and social-welfare metrics on three large-scale survey datasets from China, the U.S. and Europe. |
Content- and Topology-Aware Representation Learning for Scientific Multi-Literature (2023.emnlp-main)
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| Challenge: | Existing approaches focus on learning textual information at sentence- or document-level, but ignore inter-document connections. |
| Approach: | They propose a model that extends representation learning to the multi-document level . it integrates latent semantic and rich relatedness information from topological networks . |
| Outcome: | The proposed model learns latent semantic information from content and rich relatedness information from topological networks. |
Cat-MoD: Accelerating Multimodal Alignment via Caption Token Guided Asymmetric Mixture-of-Depths (2026.acl-long)
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| Challenge: | Existing query-based alignment modules enforce uniform cross-attention across all layers, leading to computational redundancy. |
| Approach: | They propose a framework that allows for asynchronous query-based alignment with large-scale visual features. |
| Outcome: | The proposed framework matches or surpasses baseline performance while reducing alignment FLOPs by approximately 37% during training and inference. |
Knowledge Triplets Derivation from Scientific Publications via Dual-Graph Resonance (2024.lrec-main)
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| Challenge: | Existing relation extraction methods aim to extract explicit triplet knowledge from documents, but they can hardly perceive unobserved factual relations. |
| Approach: | They propose a novel Extraction-Contextualization-Derivation strategy to generate a document-specific dynamic graph from a shared static knowledge graph. |
| Outcome: | The proposed method can generate richer explicit and implicit relations under the guidance of static and dynamic knowledge topologies. |