Papers by Jinseok Nam
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems (2022.naacl-industry)
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| Challenge: | Existing methods to enable skill routing do not scale in terms of the number of skills and skill on-boarding. |
| Approach: | They propose a model-based approach to enable natural conversation by allowing frequent policy updates . they propose an annotation-based system, rule-based model, and bandit-based learning . |
| Outcome: | The proposed method is scalable and cost-effective, the authors show . they show that it can improve the user experience without abrupt policy changes . |
Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests (2025.coling-main)
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| Challenge: | Existing task-oriented dialogue benchmarks lack sufficient examples of complex discourse phenomena such as indirectness. |
| Approach: | They propose a set of linguistic criteria and an LLM-based pipeline for generating realistic IURs to test natural language understanding and dialogue state tracking models before deployment in a new domain. |
| Outcome: | The proposed model can handle indirect user requests (IURs) but lacks examples of complex discourse phenomena such as indirectness. |