Papers by Radin Shayanfar
CoDial: Interpretable Task-Oriented Dialogue Systems Through Dialogue Flow Alignment (2026.acl-long)
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| Challenge: | Recent schema-based TOD frameworks improve generalization by decoupling task logic from language understanding, but their reliance on neural or generative models obscures how task schemas influence behaviour and hence impair interpretability. |
| Approach: | They propose a framework that converts a predefined task schema to a structured heterogeneous graph and then to popular programmatic LLM guardrailing code, such as NVIDIA’s Colang. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the widely used benchmark datasets while providing inherent interpretability in the design. |
Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation (2024.findings-emnlp)
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| Challenge: | Existing research has focused on the veracity of information, overlooking the legal implications and consequences of misinformation. |
| Approach: | They propose a task to detect misinformation using legal issues as a measure of societal ramifications. |
| Outcome: | The proposed task leverages definitions from a wide range of legal domains covering 4 broader legal topics and 11 fine-grained legal issues, including hate speech, election laws, and privacy regulations. |