Papers by Quanming Yao
Simplified Graph Learning for Inductive Short Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods for short text classification are limited and lack of labeled data is not enough. |
| Approach: | They propose a novel short text classification algorithm which leverages words to handle the lack of labeled data. |
| Outcome: | The proposed model performs better with lower memory consumption and faster inference speed than previous models. |
Superpose Task-specific Features for Model Merging (2025.emnlp-main)
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| Challenge: | Existing methods for model merging are limited by resource demands . recent studies validate the linear representation hypothesis . |
| Approach: | They propose a method that superposes task-specific features from individual models into a merged model. |
| Outcome: | The proposed method outperforms existing methods on multiple benchmarks and models. |
Nested-Refinement Metamorphosis: Reflective Evolution for Efficient Optimization of Networking Problems (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel in network algorithm design but suffer from inefficient iterative coding and high computational costs. |
| Approach: | They propose a method to iteratively refine task descriptions and metamorphosis on algorithms to generate more effective solutions. |
| Outcome: | Experimental results show that Nested-Refinement Metamorphosis outperforms state-of-the-art approaches in performance and efficiency. |
Think Both Ways: Teacher-Student Bidirectional Reasoning Enhances MCQ Generation and Distractor Quality (2025.findings-acl)
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| Challenge: | Existing methods for generating high-quality MCQs struggle with contextual relevance and plausible distractors. |
| Approach: | They propose a framework that integrates bidirectional reasoning perspectives to generate contextually relevant questions and plausible distractors while student reasoning evaluates question clarity and the misleading nature of distractors. |
| Outcome: | The proposed framework outperforms existing methods in generating text-grounded questions and high-quality distractors for narrative contexts. |
Relation-aware Ensemble Learning for Knowledge Graph Embedding (2023.emnlp-main)
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| Challenge: | Existing methods to explore semantics of knowledge graphs have been proposed to explore these semantics in distinct ways. |
| Approach: | They propose to leverage existing methods in relation-aware manner to learn an ensemble by leveraging existing methods. |
| Outcome: | The proposed method has the same computation cost as general ensemble methods but with much better performance on benchmark datasets. |
Search to Pass Messages for Temporal Knowledge Graph Completion (2022.findings-emnlp)
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| Challenge: | Recent studies on missing facts in temporal knowledge graphs are based on hand-designed architectures and fail to explore the diverse topological and temporal properties of TKGs. |
| Approach: | They propose to use neural architecture search to design a data-specific message passing architecture for TKG completion. |
| Outcome: | The proposed architectures achieve the state-of-the-art performance on three benchmark datasets. |
Efficient Hyper-parameter Search for Knowledge Graph Embedding (2022.acl-long)
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| Challenge: | Existing methods for learning knowledge graphs do not search hyper-parameters efficiently. |
| Approach: | They propose an efficient two-stage search algorithm which explores HP configurations on small subgraph and transfers top-performed configurations for fine-tuning on large full graph. |
| Outcome: | The proposed method finds better HPs than baseline algorithms within the same time budget and achieves 9.1% relative improvement on large-scale knowledge graphs. |
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification (2021.emnlp-main)
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| Challenge: | Short text classification is a fundamental task in natural language processing. |
| Approach: | They propose a new method called SHINE which is based on graph neural network for short text classification. |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark short text datasets. |