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Yuchen Han

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4 papers

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AAAI Conference 2026 Conference Paper

An LLM-based Simulation Framework for Embodied Conversational Agents in Psychological Counseling

Lixiu Wu
Yuanrong Tang
Qisen Pan
Xianyang Zhan
Yuchen Han
Lanxi Xiao
Tianhong Wang
Chen Zhong

Due to privacy concerns, open dialogue datasets for mental health are primarily generated through human or AI synthesis methods. However, the inherent implicit nature of psychological processes, particularly those of clients, poses challenges to the authenticity and diversity of synthetic data. In this paper, we propose ECAs (short for Embodied Conversational Agents), a framework for embodied agent simulation based on Large Language Models (LLMs) that incorporates multiple psychological theoretical principles. Using simulation, we expand real counseling case data into a nuanced embodied cognitive memory space and generate dialogue data based on high-frequency counseling questions. We validated our framework using the D4 dataset. First, we created a public ECAs dataset through batch simulations based on D4. Licensed counselors evaluated our method, demonstrating that it significantly outperforms baselines in simulation authenticity and necessity. Additionally, two LLM-based automated evaluation methods were employed to confirm the higher quality of the generated dialogues compared to the baselines.

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IJCAI Conference 2024 Conference Paper

Self-adaptive Extreme Penalized Loss for Imbalanced Time Series Prediction

Yiyang Wang
Yuchen Han
Yuhan Guo

Forecasting time series in imbalanced data presents a significant research challenge that requires considerable attention. Although there are specialized techniques available to tackle imbalanced time series prediction, existing approaches tend to prioritize extreme predictions at the expense of compromising the forecasting accuracy of normal samples. We in this paper propose an extreme penalized loss function that relaxes the constraint on overestimating extreme events, thereby imposing great penalties on both normal and underestimating extreme events. In addition, we provide a self-adaptive way for setting the hyperparameters of the loss function. Then, both the proposed loss function and an attention module are integrated with LSTM networks in a decomposition-based framework. Extensive experiments conducted on real-world datasets demonstrate the superiority of our framework compared to other state-of-the-art approaches for both time series prediction and block maxima prediction tasks.

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AAAI Conference 2023 Conference Paper

Unsupervised Paraphrasing under Syntax Knowledge

Tianyuan Liu
Yuqing Sun
Jiaqi Wu
Xi Xu
Yuchen Han
Cheng Li
Bin Gong

The soundness of syntax is an important issue for the paraphrase generation task. Most methods control the syntax of paraphrases by embedding the syntax and semantics in the generation process, which cannot guarantee the syntactical correctness of the results. Different from them, in this paper we investigate the structural patterns of word usages termed as the word composable knowledge and integrate it into the paraphrase generation to control the syntax in an explicit way. This syntax knowledge is pretrained on a large corpus with the dependency relationships and formed as the probabilistic functions on the word-level syntactical soundness. For the sentence-level correctness, we design a hierarchical syntax structure loss to quantitatively verify the syntactical soundness of the paraphrase against the given dependency template. Thus, the generation process can select the appropriate words with consideration on both semantics and syntax. The proposed method is evaluated on a few paraphrase datasets. The experimental results show that the quality of paraphrases by our proposed method outperforms the compared methods, especially in terms of syntax correctness.

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AAAI Conference 2020 System Paper

DRAGON-V: Detection and Recognition of Airplane Goals with Navigational Visualization

Christabel Wayllace
Sunwoo Ha
Yuchen Han
Jiaming Hu
Shayan Monadjemi
William Yeoh
Alvitta Ottley

We introduce Detection and Recognition of Airplane GOals with Navigational Visualization (DRAGON-V), a visualization system that uses probabilistic goal recognition to infer and display the most probable airport runway that a pilot is approaching. DRAGON-V is especially useful in cases of miscommunication, low visibility, or lack of airport familiarity which may result in a pilot deviating from the assigned taxiing route. The visualization system conveys relevant information, and updates according to the airplane's current geolocation. DRAGON-V aims to assist air traffic controllers in reducing incidents of runway incursions at airports.

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