Papers by Qing Liao
TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection (2026.acl-long)
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| Challenge: | Existing benchmarks for political user-level stance detection rely on noisy heuristics or distant supervision. |
| Approach: | They propose a large-scale, expert-annotated benchmark for political user-level stance detection with explicit social network structure that integrates user content and followee signals. |
| Outcome: | The proposed framework outperforms baselines in terms of quality and reliability. |
Generative Text-to-Image Retrieval via Hierarchical Identifiers and Semantic Internalization (2026.findings-acl)
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| Challenge: | Existing text-to-image retrieval methods suffer from limited semantic discriminability, alignment bias, and closed-set restrictions. |
| Approach: | They propose a framework for semantic internalization for Generative Multimodal Alignment . they construct multi-granularity hierarchical identifiers to ensure unique, semantically consistent image representations . |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on Flickr30K and MS-COCO datasets . it achieves average Recall@1, Recall @5, and Recall_10 improvements of 10.65%, 8.50%, and 7.00% . |
Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding (2026.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection. |
| Approach: | They propose a large-scale model that combines large-level annotations with hyperbolic space to model human cognitive states. |
| Outcome: | The proposed model outperforms baseline models on cognitive dimensions on single dimension tasks while retaining strong hierarchical structure. |
Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Existing methods for Aspect-based sentiment analysis (ABSA) focus on mining syntactic or semantic information, which suffers from noisy interference when multiple aspects exist in a sentence. |
| Approach: | They propose a scope-assisted multi-view graph contrastive learning framework that captures correlation and difference between aspect and syntactic/semantic information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on five benchmark datasets and verifies its effectiveness and robustness. |
Enhancing Text-to-SQL Capabilities of Large Language Models through Tailored Promptings (2024.lrec-main)
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| Challenge: | Large language models with prompting have achieved encouraging results on many natural language processing tasks due to the absence of task-tailored promptings. |
| Approach: | They propose three promptings specifically designed for Text-to-SQL: SL-prompt, CC-promped, and SL+CC prompt. |
| Outcome: | The proposed promptings achieve execution accuracy of 86.2% and test-suite accuracy of 76% . the granularity of schema linking and the order of clause generation have great impact on performance, which are considered little in previous research. |
Affective Knowledge Enhanced Multiple-Graph Fusion Networks for Aspect-based Sentiment Analysis (2022.emnlp-main)
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| Challenge: | Existing methods for sentiment analysis ignore the roles of syntax dependency relation labels and affective semantic information in determining the sentiment polarity of social media users. |
| Approach: | They propose a new multi-graph fusion network to leverage the richer syntax dependency relation labels and affective semantic information of words. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three datasets. |
FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning (2025.coling-main)
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Dongyi Zheng, Hongyu Zhang, Jianyang Zhai, Lin Zhong, Lingzhi Wang, Jiyuan Feng, Xiangke Liao, Yonghong Tian, Nong Xiao, Qing Liao
| Challenge: | Existing federated frameworks for cross-domain sequential recommendation rely on user alignment, which increases communication costs and privacy risks. |
| Approach: | They propose a federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms. |
| Outcome: | The proposed framework eliminates the need for user alignment between platforms. |
Improving Multi-task Stance Detection with Multi-task Interaction Network (2022.emnlp-main)
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| Challenge: | Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection but neglect to capture the fine-grained task-specific interaction between stance and sentiment tasks, thus degrading performance. |
| Approach: | They propose a novel multi-task interaction network (MTIN) that captures the word-level interaction between tasks, so as to obtain richer task representations. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two real-world datasets. |
Improving Gradient Trade-offs between Tasks in Multi-task Text Classification (2023.acl-long)
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| Challenge: | Existing methods to mitigate task conflict problem are heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade-off among different tasks . |
| Approach: | They propose a gradient trade-off approach to mitigate the task conflict problem by using heuristics or gradient-based algorithms to achieve an arbitrary Pareto optimal trade- off among different tasks. |
| Outcome: | The proposed model can achieve an arbitrary Pareto optimal trade-off among different tasks near the main objective of multi-task text classification (MTC) it is found that training all tasks simultaneously yields degraded performance than learning them independently, leading to poor training. |
SAGE: Synergistic Adaptive Gating of Experts for Hateful Video Detection (2026.acl-long)
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| Challenge: | Existing methods for hateful video detection rely on multimodal feature fusion . existing methods rely only on blind feature mixing, which leads to feature dilution . |
| Approach: | They propose a framework that shifts from blind feature mixing to decision-level arbitration . it instantiates disentangled experts to rigorously preserve modality-specific semantics . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on HateMM and MultiHateClip benchmarks. |
Cause-CSD: A Challenge Multimodal Conversational Stance Cause Detection Dataset and Effective Method (2026.findings-acl)
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| Challenge: | Existing stance detection methods treat opinions as surface-level labels, overlooking conversational evidence behind stance expressions. |
| Approach: | They propose a task that jointly identifies stance polarity and contextual evidence . they propose stance-cause Detection language model that leverages explicit context reasoning . |
| Outcome: | The proposed task outperforms baseline methods on text-only and multimodal subtasks. |
Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation (2026.acl-long)
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| Challenge: | Existing models for ESC ignore cognitive distortions in help-seekers' expressions . current models provide basic emotional comfort, rather than helping help- seekers address psychological distress at a deeper cognitive level. |
| Approach: | They propose a Large Language Model framework to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers. |
| Outcome: | The proposed framework outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control. |