Papers by Zitao Liu
Does Gender Matter? Towards Fairness in Dialogue Systems (2020.coling-main)
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| Challenge: | Recent studies have shown that AI is unfair in many real-world applications such as computer vision and recommendations. |
| Approach: | They propose to use a benchmark dataset to study the fairness of dialogue systems to understand their bias. |
| Outcome: | The proposed methods reduce the bias in dialogue systems significantly. |
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)
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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Foundational models and their checkpoints have advanced deep learning, boosting performance across applications. |
| Approach: | They propose a method for pruning fine-tuned models by calculating differences between them and original model. |
| Outcome: | The proposed method can improve performance across vision, NLP, and multi-modal benchmarks. |
DiffCL: Difference-Aware Contrastive Learning for Automatic Answer Grading with Multi-Level Semantic Modeling (2026.findings-acl)
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| Challenge: | Existing approaches to automate answer grading lack semantic understanding and scoring consistency. |
| Approach: | They propose a difference-aware AAG framework that integrates heuristic difference labeling with dual-contrastive learning. |
| Outcome: | The proposed method outperforms cross-entropy-based baselines on SciEntsBank and Beetle datasets. |
Learning Goal-oriented Dialogue Policy with opposite Agent Awareness (2020.aacl-main)
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| Challenge: | Existing approaches for goal-oriented dialogue policy learning focus on the target agent policy and treat the opposite agent policy as part of the environment. |
| Approach: | They propose a framework for policy learning in goal-oriented dialogues that uses the opposite agent's policy estimation to improve the target agent by regarding it as part of the target policy. |
| Outcome: | The proposed framework shows superior performance over state-of-the-art models on cooperative and competitive dialogue tasks. |
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)
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| Challenge: | Existing models for generating mathematical word problems are lacking in educational assessment. |
| Approach: | They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations. |
| Outcome: | The proposed model outperforms the SOTA models in terms of evaluation metrics and topic relevance. |
Personalized Multimodal Feedback Generation in Education (2020.coling-main)
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| Challenge: | In this paper, we propose a novel Personalized Multimodal Feedback Generation Network (PMFGN) that generates personalized feedback for teachers to evaluate assignments involving multimodal inputs. |
| Approach: | They propose a Personalized Multimodal Feedback Generation Network (PMFGN) that generates personalized feedback for teachers to evaluate assignments involving multimodal inputs such as images, audios, and texts. |
| Outcome: | The proposed model outperforms baseline models on real-world K-12 education data and detailed ablation experiments to deepen understanding of the proposed framework. |
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning (2020.emnlp-main)
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| Challenge: | Recent research shows that dialogue systems trained on human conversation data are biased and can produce responses that reflect people’s gender prejudice. |
| Approach: | They propose a novel adversarial learning framework Debiased-Chat to train dialogue models free from gender bias while keeping their performance. |
| Outcome: | The proposed framework significantly reduces gender bias in dialogue models while maintaining the response quality. |
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics (2021.acl-long)
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Jian Guan, Zhexin Zhang, Zhuoer Feng, Zitao Liu, Wenbiao Ding, Xiaoxi Mao, Changjie Fan, Minlie Huang
| Challenge: | Existing automatic metrics are observed to correlate poorly with human evaluation. |
| Approach: | They propose to use OpenMEVA to evaluate open-ended story generation metrics. |
| Outcome: | The proposed test suite assesses the capabilities of open-ended story generation metrics on annotated stories and auto-constructed test examples. |
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification (2021.findings-acl)
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| Challenge: | Existing studies on text classification have focused on the bias towards the individuals mentioned in the text content. |
| Approach: | They propose a framework to mitigate implicit bias in text classification models based on demographic attributes of authors . they propose to use this framework to train deep text classifiers to make predictions on the right features . |
| Outcome: | The proposed framework outperforms existing models significantly in fairness and performance. |
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation (2025.acl-industry)
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Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang, Zitao Shuai, Thao Nguyen Minh Phan, Jun-En Ding, Chun-Chieh Liao, Pengfei Hu, Xiaoxue Han, Chih-Ho Hsu, Dongsheng Luo, Wen-Chih Peng, Feng Liu, Fang-Ming Hung, Chenwei Wu
| Challenge: | Existing systems focus primarily on assessment rather than treatment planning. |
| Approach: | They propose a framework that structures LLM reasoning to align with real-life workflows. |
| Outcome: | The proposed framework outperforms baseline approaches in assessment accuracy and treatment plan quality. |
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence (2021.acl-long)
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| Challenge: | Existing generation models struggle to maintain a coherent event sequence throughout the generated text. |
| Approach: | They propose a long text generation model which can represent prefix sentences at sentence level and discourse level in the decoding process. |
| Outcome: | The proposed model can generate more coherent texts than state-of-the-art models. |
StatsChartMWP: A Dataset for Evaluating Multimodal Mathematical Reasoning Abilities on Math Word Problems with Statistical Charts (2025.findings-emnlp)
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| Challenge: | StatsChartMWP is a dataset for evaluating visual mathematical reasoning abilities on math word problems with statistical charts. |
| Approach: | They propose a dataset for evaluating visual mathematical reasoning abilities on math word problems with statistical charts. |
| Outcome: | The proposed model is more effective than open-source approaches. |
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations (2021.emnlp-main)
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| Challenge: | Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. |
| Approach: | They propose a cross-modal transformer for audio-and-language that learns inter-modal connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs. |
| Outcome: | The proposed model improves on multiple audio-and-language tasks and can be used in fine-tuning phase. |