Papers with generalization and interpretability
Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder (2020.acl-srw)
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| Challenge: | EE is a key requirement for machine learning in many domains, e.g., legal, medical, finance. |
| Approach: | They propose an interpretable approach for event extraction that jointly trains a classifier and a rule decoder for event processing. |
| Outcome: | The proposed approach can be used for semi-supervised learning and its performance improves when trained on automatically-labeled data generated by a rule-based system. |
Third-Person Appraisal Agent: Simulating Human Emotional Reasoning in Text with Large Language Models (2025.findings-emnlp)
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| Challenge: | Emotional reasoning is essential for improving human-AI interactions, especially in mental health support and empathetic systems. |
| Approach: | They propose a third-person appraisal agent that simulates human-like emotional reasoning through three phases: Primary Appraisal, Secondary Appraisals, and Reappraisal. |
| Outcome: | The proposed model outperforms baseline LLMs in various emotional reasoning tasks, demonstrating superior generalization and interpretability. |