Papers by Pengfei Hong
Enhancing Self-Attention with Knowledge-Assisted Attention Maps (2022.naacl-main)
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Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen
| Challenge: | Existing works of knowledge infusion depend on multi-task learning frameworks, which are inefficient and require large-scale retraining when new knowledge is considered. |
| Approach: | They propose a method which integrates knowledge-generated attention maps into the self-attention mechanism and integrates it into the model. |
| Outcome: | The proposed model outperforms existing methods on academic datasets and industry-scale ad relevance applications. |
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction (2023.acl-long)
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| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations between entities in a document. |
| Approach: | They propose a Document-level distant relation extraction framework with unreliable pseudo labels to denoise DS data. |
| Outcome: | The proposed framework outperforms strong baselines on two public datasets. |
MIME: MIMicking Emotions for Empathetic Response Generation (2020.emnlp-main)
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Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
| Challenge: | Empathy is a fundamental human trait that reflects our ability to understand and reflect the thoughts and feelings of the people we interact with. |
| Approach: | They propose to use polarity-based emotion clusters to generate empathetic responses . they also introduce stochasticity into the emotion mixture that yields emotionally more varied responses compared to the previous work . |
| Outcome: | The proposed methods improve empathy and contextual relevance of the response, and introduce stochasticity into the emotion mixture that yields emotionally more varied responses than the previous work. |
Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning (2025.acl-long)
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| Challenge: | Visual-Language-Action models lack the ability to generate actionable policies tailored to specific robotic embodiments. |
| Approach: | They propose an embodied multimodal action model with Grounded Chain of Thought and Look-ahead Spatial Reasoning that enhances spatial reasoning and task planning. |
| Outcome: | The proposed model improves on existing baselines in tasks requiring spatial reasoning and grounding reasoning. |
Evaluating LLMs’ Mathematical and Coding Competency through Ontology-guided Interventions (2025.findings-acl)
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| Challenge: | Current large language models have shown impressive performance on logical reasoning benchmarks . however, the true depth of their competencies and robustness in reasoning tasks remains an open question . |
| Approach: | They propose a general ontology of perturbations and a semi-automatic method to apply perturbations to arithmetic reasoning and code generation datasets to test their LLMs' capabilities. |
| Outcome: | The proposed model outperforms existing models on arithmetic reasoning and code generation tasks. |
A Robust Information-Masking Approach for Domain Counterfactual Generation (2023.findings-acl)
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| Challenge: | Domain shift is a big challenge in NLP, but many approaches fail to leverage domain-specific nuances relevant to the task at hand. |
| Approach: | They propose a method that uses frequency-based masking to transform a text from the source domain to a target domain. |
| Outcome: | The proposed method outperforms baselines on 10 out of 12 domain-counterfactual classification settings with an average of 1.7% improvement in accuracy metric. |
Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt (2023.findings-acl)
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| Challenge: | Existing studies require massive labeled data to train models for multimodal data analysis. |
| Approach: | They propose a novel multimodal prompt model that captures specific aspect terms in a few-shot scenario. |
| Outcome: | The proposed model outperforms baselines on two MABSA-related tasks on a few-shot dataset. |