Papers by Jeehyun Lee

2 papers
AMAN: Agent for Mentoring and Assisting Newbies in MMORPG (2025.coling-industry)

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Challenge: AMAN is a chatbot designed to help novice gamers learn the gameplay mechanics of online games.
Approach: They propose a model that functions as a human-like chat buddy that interacts with users in a friendly manner while providing substantive informational depth.
Outcome: The proposed model integrates continual pre-training with a sequence of online resources and instruction tuning on curated dialogues.
Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System (2023.findings-acl)

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Challenge: Recent work on end-to-end dialogue models with pre-trained dialogue corpora shows promising performance in the conversational system.
Approach: They propose an end-to-end TOD system with task-optimized adapters which learn independently per task adding only small number of parameters after fixed layers of pre-trained network.
Outcome: The proposed system achieves state-of-the-art performance on the MultiWOZ benchmark compared to existing models.

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