Papers by Meihong Wang
AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation (2026.acl-long)
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| Challenge: | Existing methods for aggregating large-form outputs overlook the nuance of neutral information and suffer from the high computational cost of fine-grained decomposition. |
| Approach: | They propose a UQ framework that uses NLI neutral probabilities as triggers to distinguish irrelevance from uncertainty, reducing computation costs. |
| Outcome: | Experiments on BIO and LongFact show that the proposed framework reduces inference time by 60% compared to full atomic decomposition. |
DTCRS: Dynamic Tree Construction for Recursive Summarization (2025.acl-long)
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| Challenge: | Recursive summarization (RAG) is an important method for mitigating large model hallucinations and enhancing answer interpretability. |
| Approach: | They propose a method that dynamically generates summary trees based on document structure and query semantics. |
| Outcome: | The proposed method significantly reduces summary tree construction time and achieves substantial improvements across three QA tasks. |
AGCL: Aspect Graph Construction and Learning for Aspect-level Sentiment Classification (2025.coling-main)
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| Challenge: | Aspect-level Sentiment Classification (ALSC) is a fine-grained sentiment analysis task that aims to identify the sentiment polarity of a review text toward each corresponding aspect. |
| Approach: | They propose a novel Aspect Graph Construction and Learning method that harnesses aspect connections to construct a domain aspect graph and iteratively updates it to enhance its domain expertise. |
| Outcome: | The proposed method can bridge unseen aspects with seen aspects, enhancing the model's generalization capability. |
GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question Answering (2026.findings-acl)
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| Challenge: | Recent work on Chain-of-Thought reasoning requires manual prompts to guide the model. |
| Approach: | They propose a general decoding strategy that generates CoT-style reasoning paths without prompts. |
| Outcome: | The proposed method maintains strong performance on fixed and free QA tasks and achieves significant improvements on free qa. |
EmoTrans: Emotional Transition-based Model for Emotion Recognition in Conversation (2024.lrec-main)
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| Challenge: | Emotions are causally transmitted among communication participants, facilitating comprehension of intricate changes in emotional states during the conversation. |
| Approach: | They propose an Emotional Transition-based Emotion Recognizer that captures ET features in an emotional conversation by concatenating the most recent utterances with their corresponding speakers. |
| Outcome: | The proposed model is sensitive to emotions and captures ET features in the sample. |