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Changlong Sun

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15 papers

2 author rows

AAAI Conference 2026 Conference Paper

P2S: Probabilistic Process Supervision for General-Domain Reasoning Question Answering

Wenlin Zhong
Chengyuan Liu
Yiquan Wu
Bovin Tan
Changlong Sun
Yi Wang
Xiaozhong Liu
Kun Kuang

While reinforcement learning with verifiable rewards (RLVR) has advanced LLM reasoning in structured domains like mathematics and programming, its application to general-domain reasoning tasks remains challenging due to the absence of verifiable reward signals. To this end, methods like Reinforcement Learning with Reference Probability Reward (RLPR) have emerged, leveraging the probability of generating the final answer as a reward signal. However, these outcome-focused approaches neglect crucial step-by-step supervision of the reasoning process itself. To address this gap, we introduce Probabilistic Process Supervision (P2S), a novel self-supervision framework that provides fine-grained process rewards without requiring a separate reward model or human-annotated reasoning steps. During reinforcement learning, P2S synthesizes and filters a high-quality reference reasoning chain (gold-CoT). The core of our method is to calculate a Path Faithfulness Reward (PFR) for each reasoning step, which is derived from the conditional probability of generating the gold-CoT's suffix, given the model's current reasoning prefix. Crucially, this PFR can be flexibly integrated with any outcome-based reward, directly tackling the reward sparsity problem by providing dense guidance. Extensive experiments on reading comprehension and medical Question Answering benchmarks show that P2S significantly outperforms strong baselines.

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AAAI Conference 2026 Conference Paper

Think Then Rewrite: Reasoning Enhanced Query Rewriting for Domain Specific Retrieval

Ang Li
Yufei Shi
Yuxuan Si
Yiquan Wu
Ming Cai
Xu Tan
Yi Wang
Changlong Sun

Query rewriting is a crucial task for improving retrieval, especially in professional domains such as law and medicine, where user queries are often underspecified and ambiguous. While large language models (LLMs) offer strong understanding and generation capabilities, existing LLM-based approaches reduce the task to text transformation or expansion, neglecting reasoning to disambiguate queries, which fails to bridge the cognitive gap between user queries and specialized documents. In this paper, we propose Think-Then-Rewrite (TTR), a reinforcement learning based framework that unleashes LLMs' reasoning ability for domain-specific query rewriting. TTR introduces a contrastive mutual information reward to encourage the LLM to generate reasoning processes that effectively distinguish confusing distractors. To boost early-stage training, TTR also constructs golden query rewrites as off‑policy data, providing strong guidance for RL learning. A mixed-policy optimization then combines on-policy and off-policy signals, ensuring both effectiveness and stability. Extensive experiments on legal and medical retrieval benchmarks demonstrate that TTR achieves state-of-the-art performance.

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AAAI Conference 2024 Conference Paper

De-biased Attention Supervision for Text Classification with Causality

Yiquan Wu
Yifei Liu
Ziyu Zhao
Weiming Lu
Yating Zhang
Changlong Sun
Fei Wu
Kun Kuang

In text classification models, while the unsupervised attention mechanism can enhance performance, it often produces attention distributions that are puzzling to humans, such as assigning high weight to seemingly insignificant conjunctions. Recently, numerous studies have explored Attention Supervision (AS) to guide the model toward more interpretable attention distributions. However, such AS can impact classification performance, especially in specialized domains. In this paper, we address this issue from a causality perspective. Firstly, we leverage the causal graph to reveal two biases in the AS: 1) Bias caused by the label distribution of the dataset. 2) Bias caused by the words' different occurrence ranges that some words can occur across labels while others only occur in a particular label. We then propose a novel De-biased Attention Supervision (DAS) method to eliminate these biases with causal techniques. Specifically, we adopt backdoor adjustment on the label-caused bias and reduce the word-caused bias by subtracting the direct causal effect of the word. Through extensive experiments on two professional text classification datasets (e.g., medicine and law), we demonstrate that our method achieves improved classification accuracy along with more coherent attention distributions.

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AAAI Conference 2024 Conference Paper

Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery

Pengwei Yan
Kaisong Song
Zhuoren Jiang
Yangyang Kang
Tianqianjin Lin
Changlong Sun
Xiaozhong Liu

While self-supervised graph pretraining techniques have shown promising results in various domains, their application still experiences challenges of limited topology learning, human knowledge dependency, and incompetent multi-level interactions. To address these issues, we propose a novel solution, Dual-level Graph self-supervised Pretraining with Motif discovery (DGPM), which introduces a unique dual-level pretraining structure that orchestrates node-level and subgraph-level pretext tasks. Unlike prior approaches, DGPM autonomously uncovers significant graph motifs through an edge pooling module, aligning learned motif similarities with graph kernel-based similarities. A cross-matching task enables sophisticated node-motif interactions and novel representation learning. Extensive experiments on 15 datasets validate DGPM's effectiveness and generalizability, outperforming state-of-the-art methods in unsupervised representation learning and transfer learning settings. The autonomously discovered motifs demonstrate the potential of DGPM to enhance robustness and interpretability.

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AAAI Conference 2023 Conference Paper

A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis

Kaisong Song
Yangyang Kang
Jiawei Liu
Xurui Li
Changlong Sun
Xiaozhong Liu

User Satisfaction Estimation is an important task and increasingly being applied in goal-oriented dialogue systems to estimate whether the user is satisfied with the service. It is observed that whether the user’s needs are met often triggers various sentiments, which can be pertinent to the successful estimation of user satisfaction, and vice versa. Thus, User Satisfaction Estimation (USE) and Sentiment Analysis (SA) should be treated as a joint, collaborative effort, considering the strong connections between the sentiment states of speakers and the user satisfaction. Existing joint learning frameworks mainly unify the two highly pertinent tasks over cascade or shared-bottom implementations, however they fail to distinguish task-specific and common features, which will produce sub-optimal utterance representations for downstream tasks. In this paper, we propose a novel Speaker Turn-Aware Multi-Task Adversarial Network (STMAN) for dialogue-level USE and utterance-level SA. Specifically, we first introduce a multi-task adversarial strategy which trains a task discriminator to make utterance representation more task-specific, and then utilize a speaker-turn aware multi-task interaction strategy to extract the common features which are complementary to each task. Extensive experiments conducted on two real-world service dialogue datasets show that our model outperforms several state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

Unsupervised Legal Evidence Retrieval via Contrastive Learning with Approximate Aggregated Positive

Feng Yao
Jingyuan Zhang
Yating Zhang
Xiaozhong Liu
Changlong Sun
Yun Liu
Weixing Shen

Verifying the facts alleged by the prosecutors before the trial requires the judges to retrieve evidence within the massive materials accompanied. Existing Legal AI applications often assume the facts are already determined and fail to notice the difficulty of reconstructing them. To build a practical Legal AI application and free the judges from the manually searching work, we introduce the task of Legal Evidence Retrieval, which aims at automatically retrieving the precise fact-related verbal evidence within a single case. We formulate the task in a dense retrieval paradigm, and jointly learn the constrastive representations and alignments between facts and evidence. To get rid of the tedious annotations, we construct an approximated positive vector for a given fact by aggregating a set of evidence from the same case. An entropy-based denoise technique is further applied to mitigate the impact of false positive samples. We train our models on tens of thousands of unlabeled cases and evaluate them on a labeled dataset containing 919 cases and 4,336 queries. Experimental results indicate that our approach is effective and outperforms other state-of-the-art representation and retrieval models. The dataset and code are available at https://github.com/yaof20/LER.

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AAAI Conference 2021 Conference Paper

Evidence Aware Neural Pornographic Text Identification for Child Protection

Kaisong Song
Yangyang Kang
Wei Gao
Zhe Gao
Changlong Sun
Xiaozhong Liu

Identifying pornographic text online is practically useful to protect children from access to such adult content. However, some authors may intentionally avoid using sensitive words in their pornographic texts to take advantage of the lack of human audits. Without prior knowledge guidance, real semantics of such pornographic text is difficult to understand by existing methods due to its high context-sensitivity and heavy usage of figurative language, which brings huge challenges to the porn detection systems used in social media platforms. In this paper, we approach to the problem as a document-level porn identification task by locating and integrating sentencelevel evidence and propose a novel Evidence-Aware Neural Porn Classification (eNPC) model. Specifically, we first propose a basic model which locates porn indicative sentences in the document with a multiple instance learning model, and then aggregate the sentence-level evidence to induce document label with self-attention mechanism. Moreover, we consider label dependencies within local context. Finally, we further enhance the sentence representation with prior knowledge produced by an automatic porn lexicon construction strategy. Extensive experimental results show that our model exhibits consistent superiority over competitors on two realworld Chinese novel datasets and an English story dataset.

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AAAI Conference 2021 Conference Paper

Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff

Jiawei Liu
Zhe Gao
Yangyang Kang
Zhuoren Jiang
Guoxiu He
Changlong Sun
Xiaozhong Liu
Wei Lu

Is chatbot able to completely replace the human agent? The short answer could be – “it depends. .. ”. For some challenging cases, e. g. , dialogue’s topical spectrum spreads beyond the training corpus coverage, the chatbot may malfunction and return unsatisfied utterances. This problem can be addressed by introducing the Machine-Human Chatting Handoff (MHCH) which enables human-algorithm collaboration. To detect the normal/transferable utterances, we propose a Difficulty-Assisted Matching Inference (DAMI) network, utilizing difficulty-assisted encoding to enhance the representations of utterances. Moreover, a matching inference mechanism is introduced to capture the contextual matching features. A new evaluation metric, Golden Transfer within Tolerance (GT-T), is proposed to assess the performance by considering the tolerance property of the MHCH. To provide insights into the task and validate the proposed model, we collect two new datasets. Extensive experimental results are presented and contrasted against a series of baseline models to demonstrate the efficacy of our model on MHCH.

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AAAI Conference 2021 Conference Paper

Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling

Yicheng Zou
Lujun Zhao
Yangyang Kang
Jun Lin
Minlong Peng
Zhuoren Jiang
Changlong Sun
Qi Zhang

In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries that preserve the main ideas from dialogues. In spoken dialogues, abundant dialogue noise and common semantics could obscure the underlying informative content, making the general topic modeling approaches difficult to apply. In addition, for customer service, role-specific information matters and is an indispensable part of a summary. To effectively perform topic modeling on dialogues and capture multi-role information, in this work we propose a novel topic-augmented two-stage dialogue summarizer (TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues. Comprehensive studies on a real-world Chinese customer service dataset demonstrated the superiority of our method against several strong baselines.

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AAAI Conference 2021 Conference Paper

Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders

Yicheng Zou
Jun Lin
Lujun Zhao
Yangyang Kang
Zhuoren Jiang
Changlong Sun
Qi Zhang
Xuanjing Huang

Automatic chat summarization can help people quickly grasp important information from numerous chat messages. Unlike conventional documents, chat logs usually have fragmented and evolving topics. In addition, these logs contain a quantity of elliptical and interrogative sentences, which make the chat summarization highly context dependent. In this work, we propose a novel unsupervised framework called RankAE to perform chat summarization without employing manually labeled data. RankAE consists of a topic-oriented ranking strategy that selects topic utterances according to centrality and diversity simultaneously, as well as a denoising auto-encoder that is carefully designed to generate succinct but contextinformative summaries based on the selected utterances. To evaluate the proposed method, we collect a large-scale dataset of chat logs from a customer service environment and build an annotated set only for model evaluation. Experimental results show that RankAE significantly outperforms other unsupervised methods and is able to generate high-quality summaries in terms of relevance and topic coverage.

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ECAI Conference 2020 Conference Paper

Behavior Based Dynamic Summarization on Product Aspects via Reinforcement Neighbour Selection

Zheng Gao 0001
Lujun Zhao
Heng Huang
Hongsong Li
Changlong Sun
Luo Si
Xiaozhong Liu 0001

Dynamic summarization on product aspects, as a newly proposed topic, is an important task in E-commerce for tracking and understanding the nature of products. This can benefit both customers and sellers in different downstream tasks, such as explainable recommendations. However, most existing research works focus on analyzing product static reviews but miss dynamic sentiment changes. In this paper, we propose an innovative multi-task model to sample neighbour products whose information is simultaneously utilized to generate product summarization. In detail, a reinforcement learning approach selects neighbour products from a group of seed products by considering their pairwise similarities calculated from user behaviors. Meanwhile, a generative model helps to summarize product aspects via product descriptive phrases and selected neighbour products’ sentimental phrases. To the best of our knowledge, this is the first work that studies dynamic product summarization leveraging user behaviors instead of self-reviews. It means that the proposed approach can naturally address the cold-start scenario where few recent product reviews are available. Extensive experiments are conducted with real-world reviews plus behavior data to validate the proposed method against several strong alternatives.

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AAAI Conference 2020 Conference Paper

Masking Orchestration: Multi-Task Pretraining for Multi-Role Dialogue Representation Learning

Tianyi Wang
Yating Zhang
Xiaozhong Liu
Changlong Sun
Qiong Zhang

Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classiﬁcation, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for speciﬁc learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. Meanwhile, in order to locate essential information for dialogue summarization/extraction, the pretraining process enables external knowledge integration. The proposed ﬁne-tuned pretraining mechanism is comprehensively evaluated via three different dialogue datasets along with a number of downstream dialogue-mining tasks. Result shows that the proposed pretraining mechanism signiﬁcantly contributes to all the downstream tasks without discrimination to different encoders.

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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.

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IJCAI Conference 2019 Conference Paper

Cold-Start Aware Deep Memory Network for Multi-Entity Aspect-Based Sentiment Analysis

Kaisong Song
Wei Gao
Lujun Zhao
Jun Lin
Changlong Sun
Xiaozhong Liu

Various types of target information have been considered in aspect-based sentiment analysis, such as entities and aspects. Existing research has realized the importance of targets and developed methods with the goal of precisely modeling their contexts via generating target-specific representations. However, all these methods ignore that these representations cannot be learned well due to the lack of sufficient human-annotated target-related reviews, which leads to the data sparsity challenge, a. k. a. cold-start problem here. In this paper, we focus on a more general multiple entity aspect-based sentiment analysis (ME-ABSA) task which aims at identifying the sentiment polarity of different aspects of multiple entities in their context. Faced with severe cold-start scenario, we develop a novel and extensible deep memory network framework with cold-start aware computational layers which use frequency-guided attention mechanism to accentuate on the most related targets, and then compose their representations into a complementary vector for enhancing the representations of cold-start entities and aspects. To verify the effectiveness of the framework, we instantiate it with a concrete context encoding method and then apply the model to the ME-ABSA task. Experimental results conducted on two public datasets demonstrate that the proposed approach outperforms state-of-the-art baselines on ME-ABSA task.

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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.

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