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Hongda Sun

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5 papers

1 author row

AAAI Conference 2025 Conference Paper

BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking

Yuxuan Liu
Hongda Sun
Wenya Guo
Xinyan Xiao
Cunli Mao
Zhengtao Yu
Rui Yan

Complex claim fact-checking performs a crucial role in disinformation detection. However, existing fact-checking methods struggle with claim vagueness, specifically in effectively handling latent information and complex relations within claims. Moreover, evidence redundancy, where non-essential information complicates the verification process, remains a significant issue. To tackle these limitations, we propose Bilateral Defusing Verification (BiDeV), a novel fact-checking working-flow framework integrating multiple role-played LLMs to mimic the human-expert fact-checking process. BiDeV consists of two main modules: Vagueness Defusing identifies latent information and resolves complex relations to simplify the claim, and Redundancy Defusing eliminates redundant content to enhance the evidence quality. Extensive experimental results on two widely used challenging fact-checking benchmarks (Hover and Feverous-s) demonstrate that our BiDeV can achieve the best performance under both gold and open settings. This highlights the effectiveness of BiDeV in handling complex claims and ensuring precise fact-checking.

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AAAI Conference 2024 Conference Paper

Collaborative Synthesis of Patient Records through Multi-Visit Health State Inference

Hongda Sun
Hongzhan Lin
Rui Yan

Electronic health records (EHRs) have become the foundation of machine learning applications in healthcare, while the utility of real patient records is often limited by privacy and security concerns. Synthetic EHR generation provides an additional perspective to compensate for this limitation. Most existing methods synthesize new records based on real EHR data, without consideration of different types of events in EHR data, which cannot control the event combinations in line with medical common sense. In this paper, we propose MSIC, a Multi-visit health Status Inference model for Collaborative EHR synthesis to address these limitations. First, we formulate the synthetic EHR generation process as a probabilistic graphical model and tightly connect different types of events by modeling the latent health states. Then, we derive a health state inference method tailored for the multi-visit scenario to effectively utilize previous records to synthesize current and future records. Furthermore, we propose to generate medical reports to add textual descriptions for each medical event, providing broader applications for synthesized EHR data. For generating different paragraphs in each visit, we incorporate a multi-generator deliberation framework to collaborate the message passing of multiple generators and employ a two-phase decoding strategy to generate high-quality reports. Our extensive experiments on the widely used benchmarks, MIMIC-III and MIMIC-IV, demonstrate that MSIC advances state-of-the-art results on the quality of synthetic data while maintaining low privacy risks.

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AAAI Conference 2023 Conference Paper

ConvNTM: Conversational Neural Topic Model

Hongda Sun
Quan Tu
Jinpeng Li
Rui Yan

Topic models have been thoroughly investigated for multiple years due to their great potential in analyzing and understanding texts. Recently, researchers combine the study of topic models with deep learning techniques, known as Neural Topic Models (NTMs). However, existing NTMs are mainly tested based on general document modeling without considering different textual analysis scenarios. We assume that there are different characteristics to model topics in different textual analysis tasks. In this paper, we propose a Conversational Neural Topic Model (ConvNTM) designed in particular for the conversational scenario. Unlike the general document topic modeling, a conversation session lasts for multiple turns: each short-text utterance complies with a single topic distribution and these topic distributions are dependent across turns. Moreover, there are roles in conversations, a.k.a., speakers and addressees. Topic distributions are partially determined by such roles in conversations. We take these factors into account to model topics in conversations via the multi-turn and multi-role formulation. We also leverage the word co-occurrence relationship as a new training objective to further improve topic quality. Comprehensive experimental results based on the benchmark datasets demonstrate that our proposed ConvNTM achieves the best performance both in topic modeling and in typical downstream tasks within conversational research (i.e., dialogue act classification and dialogue response generation).

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NeurIPS Conference 2022 Conference Paper

Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records

Hongda Sun
Shufang Xie
Shuqi Li
Yuhan Chen
Ji-Rong Wen
Rui Yan

AI-empowered drug recommendation has become an important task in healthcare research areas, which offers an additional perspective to assist human doctors with more accurate and more efficient drug prescriptions. Generally, drug recommendation is based on patients' diagnosis results in the electronic health records. We assume that there are three key factors to be addressed in drug recommendation: 1) elimination of recommendation bias due to limitations of observable information, 2) better utilization of historical health condition and 3) coordination of multiple drugs to control safety. To this end, we propose DrugRec, a causal inference based drug recommendation model. The causal graphical model can identify and deconfound the recommendation bias with front-door adjustment. Meanwhile, we model the multi-visit in the causal graph to characterize a patient's historical health conditions. Finally, we model the drug-drug interactions (DDIs) as the propositional satisfiability (SAT) problem, and solving the SAT problem can help better coordinate the recommendation. Comprehensive experiment results show that our proposed model achieves state-of-the-art performance on the widely used datasets MIMIC-III and MIMIC-IV, demonstrating the effectiveness and safety of our method.

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NeurIPS Conference 2021 Conference Paper

Stylized Dialogue Generation with Multi-Pass Dual Learning

Jinpeng Li
Yingce Xia
Rui Yan
Hongda Sun
Dongyan Zhao
Tie-Yan Liu

Stylized dialogue generation, which aims to generate a given-style response for an input context, plays a vital role in intelligent dialogue systems. Considering there is no parallel data between the contexts and the responses of target style S 1, existing works mainly use back translation to generate stylized synthetic data for training, where the data about context, target style S 1 and an intermediate style S 0 is used. However, the interaction among these texts is not fully exploited, and the pseudo contexts are not adequately modeled. To overcome the above difficulties, we propose multi-pass dual learning (MPDL), which leverages the duality among the context, response of style S 1 and response of style S_0. MPDL builds mappings among the above three domains, where the context should be reconstructed by the MPDL framework, and the reconstruction error is used as the training signal. To evaluate the quality of synthetic data, we also introduce discriminators that effectively measure how a pseudo sequence matches the specific domain, and the evaluation result is used as the weight for that data. Evaluation results indicate that our method obtains significant improvement over previous baselines.

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