Papers by Kyong-Ho Lee
Data-Efficient Adaptation to Contextual Shifts in LLM-based Conversational Recommendation (2026.findings-acl)
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| Challenge: | Existing data selection methods struggle to distinguish learnable samples under contextual shifts. |
| Approach: | They propose a framework agnostic to underlying large language model-based conversational recommender systems (CRSs) that captures user preferences through free-form conversations and generates contextually relevant recommendations. |
| Outcome: | The proposed framework outperforms baselines on three CRS benchmarks with real-world temporal splits. |
Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement (2026.findings-acl)
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| Challenge: | Existing KGQA benchmarks and methods are biased toward positive and calculation constraints. Negative constraints are neglected, although they frequently appear in real-world questions. |
| Approach: | They propose a task where each question contains at least one negative constraint and a corresponding dataset, NestKGQA. |
| Outcome: | The proposed framework outperforms baselines on both KGQA and NEST-KGQA benchmarks under few-shot settings. |
Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information (D19-1)
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| Challenge: | Existing methods for sentence ordering are based on pairwise strategies. |
| Approach: | They propose a topic-guided coherence modeling (TGCM) for sentence ordering that utilizes sentence vectors in a permutation-invariant manner. |
| Outcome: | The proposed model outperforms state-of-the-art models from various perspectives. |
Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation (2023.eacl-main)
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Donghyun Kim, Youbin Ahn, Wongyu Kim, Chanhee Lee, Kyungchan Lee, Kyong-Ho Lee, Jeonguk Kim, Donghoon Shin, Yeonsoo Lee
| Challenge: | Existing researches have focused on generating diverse and consistent responses based on personal traits. |
| Approach: | They propose a consistent persona expansion framework that improves not only the diversity but also the consistency of persona-based responses. |
| Outcome: | The proposed framework improves not only the diversity but also the consistency of persona-based responses on the Persona-Chat dataset. |
Concept-based Persona Expansion for Improving Diversity of Persona-Grounded Dialogue (2023.eacl-main)
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| Challenge: | Existing approaches to improve the quality of persona-grounded dialogues are limited to a few informative words. |
| Approach: | They propose a concept-based persona expansion framework that takes the original persona as input and generates expanded personas that contain conceptually rich content. |
| Outcome: | The proposed framework improves the quality of persona-grounded dialogue responses in diversity and richness. |
LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)
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| Challenge: | Knowledge graphs (KGs) represent real-world entities and their relations in a structured form. |
| Approach: | They propose a framework that performs triple-level refinement on KGs constructed via GKE. |
| Outcome: | The proposed framework improves KG quality from diverse perspectives. |
CLICK: Contrastive Learning for Injecting Contextual Knowledge to Conversational Recommender System (2023.eacl-main)
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| Challenge: | Existing CRSs lack capturing comprehensive user preferences . existing systems lack contextual knowledge to capture user preferences from a dialogue context . |
| Approach: | They propose a Contrastive Learning approach for Injecting Contextual Knowledge from Reddit data to a CRS task. |
| Outcome: | The proposed approach captures a user preference from a dialogue context without items . it improves on the existing methods, and the results are published in the journal of cognitive science. |
Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances (2022.naacl-main)
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| Challenge: | Existing approaches for recognizing feature transitions between utterances extract features for the context at the coarse-grained level. |
| Approach: | They propose a method to recognize feature transitions between utterances that helps understand dialogue flow . they propose empathetic response generation strategy to focus on emotion and keywords related to appropriate features when generating responses. |
| Outcome: | The proposed approach outperforms baseline approaches and improves on multi-turn dialogues. |