Papers by Qingbao Huang
Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline (2025.findings-emnlp)
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| Challenge: | Existing retrieval methods neglect the execution sequence structures inherent in procedural documents. |
| Approach: | They propose a retrieval model which integrates procedural graphs with document representations. |
| Outcome: | The proposed model integrates procedural graphs with document representations to improve document retrieval. |
Unlocking Human-Like Visible Logic: How Logic Diagrams Boost Logic Reasoning in Large Language Models? (2026.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated their remarkable capabilities in natural language understanding and generation, but they struggle with formal logical reasoning. |
| Approach: | They propose to incorporate visual logic diagrams into LLMs’ reasoning workflows to enhance their performance on formal logic tasks. |
| Outcome: | The proposed model improves on syllogistic and conditional reasoning with programmatically generated Venn, Euler, and Linear diagrams. |
IgSEG: Image-guided Story Ending Generation (2021.findings-acl)
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| Challenge: | Existing tasks such as story ending generation generate text-based story endings, but visual storytelling generates photo-streams-based stories. |
| Approach: | They propose a task called Image-guided Story Ending Generation (IgSEG) given a multi-sentence story plot and an ending-related image, they propose MGCL to solve these challenges. |
| Outcome: | The proposed model outperforms baselines on automatic and human evaluation. |
Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension (2023.findings-acl)
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| Challenge: | Existing methods employ sentence-level retrieval and fusion methods, which may lead to similarity bias and interference from irrelevant information in unstructured knowledge sentences. |
| Approach: | They propose a segment-level and category-oriented network to solve similarity bias problem by segmenting and prompting knowledge retrieval methods and a category-based grounding method. |
| Outcome: | The proposed model eliminates similarity bias and improves the overall performance of the KB-REC task. |
Multi-modal Sarcasm Generation: Dataset and Solution (2023.findings-acl)
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| Challenge: | Existing studies on sarcasm generation do not consider generating sarcasastic descriptions for a given image with hashtags that provide the sarkastic target. |
| Approach: | They propose a multi-modal Sarcasm generation task that generates sarcastic descriptions like humans using images, hashtags, and OCR tokens. |
| Outcome: | The proposed method can generate sarcastic descriptions like humans using 5000 images and Twitter text. |
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering (2020.acl-main)
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| Challenge: | Existing graph-based methods focus only on relations between objects in an image and neglect the importance of syntactic dependency relations between words. |
| Approach: | They propose a dual channel graph convolutional network to capture relations between objects in an image and syntactic dependency relations between words in a question. |
| Outcome: | The proposed model achieves comparable performance with the state-of-the-art approaches. |
Can ChatGPT’s Performance be Improved on Verb Metaphor Detection Tasks? Bootstrapping and Combining Tacit Knowledge (2024.acl-long)
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| Challenge: | Existing methods for supervised metaphor detection are limited by their performance. |
| Approach: | They propose to use ChatGPT to detect most prevalent verb metaphors among metaphors . they use literal collocations of target verbs and subject-object pairs of verbs to detect them . |
| Outcome: | The proposed method achieves the best performance on the unsupervised verb metaphors detection task compared to existing unsupervised methods or direct prediction using ChatGPT. |
Merely Judging Metaphor is Not Enough: Research on Reasonable Metaphor Detection (2024.findings-emnlp)
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| Challenge: | Current metaphor detection tasks only provide labels without interpreting how to understand them. |
| Approach: | They propose to improve the current metaphor detection task by using mainstream Large Language Models. |
| Outcome: | The proposed model is based on the original sentence, target word, and usage . the model is then evaluated using manual evaluation . |
CMIG: Conceptual Metaphor Theory-Inspired Framework for Metaphorical Image Generation (2026.findings-acl)
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| Challenge: | Existing text-to-image systems often produce visually plausible but semantically literal outputs. |
| Approach: | They propose a structured prompting framework inspired by Conceptual Metaphor Theory . they propose to identify source–target mappings, filter projectable source attributes and select a visual realization strategy in a reproducible reasoning workflow. |
| Outcome: | The proposed framework improves semantic alignment and controllability on metaphor prompts. |
DAGS: A Dependency-Based Dual-Attention and Global Semantic Improvement Framework for Metaphor Recognition (2025.findings-acl)
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| Challenge: | Existing methods for metaphor recognition ignore interference caused by literal annotations . et al., 2018: Metaphor recognition plays an important role in cognition and communication . |
| Approach: | They propose a dependency-based Dual-Attention and Global Semantic Improvement framework to improve metaphor recognition. |
| Outcome: | The proposed framework can extract features from multiple information sources while improving on mainstream metaphor datasets. |