Papers by Hao Teng
Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge (2025.acl-long)
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| Challenge: | Existing methods for hyperbole and metaphor detection focus on superficial text features, ignoring the associations of hyperbola and metaphor . Existing frameworks focus on identifying superficial text, focusing on superficial features . |
| Approach: | They propose an emotion-guided hyperbole and metaphor detection framework based on bidirectional dynamic interaction. |
| Outcome: | The proposed framework outperforms baseline methods on four datasets. |
Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive translation models struggle with document context and handling discourse phenomena. |
| Approach: | They propose a simple but effective design of sentence alignment between source and target to improve their performance on document-level machine translation. |
| Outcome: | The proposed model achieves high acceleration on documents and sentence alignment significantly enhances their performance. |
Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing (2024.acl-long)
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| Challenge: | Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency graph parsing due to the internal structures of spans neglected. |
| Approach: | They propose to use latent spans as latent subtrees to model internal structures of spans and leverage TreeCRFs to extract the complete opinion tuple from a sentence. |
| Outcome: | The proposed method performs significantly better than all previous bi-lexical methods, achieving new state-of-the-art. |
ERCThinker: Fast-Slow Thinking for Emotion Recognition in Conversation (2026.acl-long)
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| Challenge: | Existing methods for ERC lack interpretability and shallow semantics capture deep semantics. |
| Approach: | They propose a Fast-Slow thinking framework for Emotion Recognition in Conversation . they use fine-grained emotion reasoning chains to capture deep semantics . |
| Outcome: | The proposed framework achieves state-of-the-art in explanation and judgment on a benchmark dataset. |
Dynamic Emotion and Personality Profiling for Multimodal Deception Detection (2026.acl-long)
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| Challenge: | Existing methods for deception detection lack sample-level dynamic annotations for emotions and personality. |
| Approach: | They propose a multi-model multi-prompt annotation scheme and a strict label quality evaluation standard for deception, emotion, and personality annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the MDPE and DDEP datasets. |
JoPR: Joint Emotion Perception and Reasoning for Conversational Emotion Recognition (2026.acl-long)
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| Challenge: | Existing methods for ERC lack human-like emotion reasoning and discrimination between similar emotions. |
| Approach: | They propose a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning for conversational emotion recognition. |
| Outcome: | The proposed model outperforms existing methods on three widely used datasets and shows that it is more intuitive and more accurate. |
What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)
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Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji
| Challenge: | Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields. |
| Approach: | They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies. |
| Outcome: | The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges. |
Spectral Disentanglement: Rank-Aware Task Adaptation for Rehearsal-free Continual Learning in LLMs (2026.acl-long)
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| Challenge: | Continual Learning (CL) for Large Language Models faces a fundamental Stability-Plasticity Dilemma . Rank-Blindness enforces a single rank constraint across diverse tasks, leading to catastrophic forgetting of earlier tasks and underfitting on complex new ones. |
| Approach: | They propose a rank-spectrum-based rehearsal-free framework that explicitly disentangles knowledge into two orthogonal subspaces. |
| Outcome: | The proposed framework achieves a superior stability-plasticity balance compared to single-rank baselines. |
MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents (2026.acl-long)
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| Challenge: | a recent study shows that VLA models suffer from temporal myopia that discards historical dynamics and reasoning gaps between high-level instructions and low-level motor commands. |
| Approach: | They propose a framework to address temporal myopia and autoregressive scalar decoding in VLAs . they propose two memory hubs that compress long-term scene evolution and short-term motion trends . |
| Outcome: | The proposed framework achieves state-of-the-art performance and exhibiting emergent error recovery capabilities. |