Papers by Md Zakir Hossain

2 papers
Thesis Proposal: Detecting Empathy Using Multimodal Language Model (2024.eacl-srw)

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Challenge: Existing studies on empathy detection in video and audio have relied on scripted or semi-scripted interactions that fail to capture the complexities and nuances of real-life interactions.
Approach: They propose to develop a multimodal language model that detects empathy in audiovisual data by using neural architecture search and optimisation techniques.
Outcome: The proposed model will be able to detect empathy in audiovisual data and use neural architecture search to deliver it.
LLM-GEm: Large Language Model-Guided Prediction of People’s Empathy Levels towards Newspaper Article (2024.findings-eacl)

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Challenge: Empathy is a key component of human-to-human interactions, and is often overlooked due to the inherent noise in crowdsourced annotations.
Approach: They propose a large language model-guided empathy prediction system that rectifies annotation errors based on defined annotation selection threshold and makes annotations reliable for conventional empathy prediction models.
Outcome: The proposed system rectifies annotation errors based on defined selection threshold and makes the annotations reliable for conventional empathy prediction models, e.g., BERT-based pre-trained language models.

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