Papers by Chanhee Park
Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents (2025.emnlp-main)
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| Challenge: | Current efforts to bridge the two modes of interaction are reactive, focusing on responding to user inputs rather than coordinating dialogue flows. |
| Approach: | They propose a dataset designed for transition-aware dialogue modeling that incorporates structurally diverse and integrated mode flows. |
| Outcome: | The proposed dataset outperforms baseline models in intent detection and mode transition handling. |
Hyper-BTS Dataset: Scalability and Enhanced Analysis of Back TranScription (BTS) for ASR Post-Processing (2024.findings-eacl)
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Chanjun Park, Jaehyung Seo, Seolhwa Lee, Junyoung Son, Hyeonseok Moon, Sugyeong Eo, Chanhee Lee, Heuiseok Lim
| Challenge: | Automatic Speech Recognition (ASR) post-processing requires substantial amounts of data, requiring expensive phonetic transcription experts. |
| Approach: | They propose a "Hyper-BTS" dataset that is five times larger than prior studies . they propose criteria for categorizing error types within ASR post-processing . |
| Outcome: | The proposed method can generate ASR inputs from clean text using a text-to-speech system. |
BlendX: Complex Multi-Intent Detection with Blended Patterns (2024.lrec-main)
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| Challenge: | Existing datasets such as MixATIS and MixSNIPS have limitations in their formulation. |
| Approach: | They propose a set of multi-intent detection datasets that feature more diverse patterns than their predecessors. |
| Outcome: | The proposed datasets feature more diverse patterns than their predecessors and are more complex and diverse than existing datasets. |
MIRAGE: A Metric-Intensive Benchmark for Retrieval-Augmented Generation Evaluation (2025.findings-naacl)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems are limited in their evaluation due to the intricate interplay between retrieval and generation components. |
| Approach: | They propose a Question Answering Question Answerer dataset specifically designed for RAG evaluation that integrates external, non-parametric knowledge retrieved by a retrieval pool of 37,800 entries. |
| Outcome: | The proposed dataset consists of 7,560 curated instances mapped to a retrieval pool of 37,800 entries, enabling an efficient evaluation of both retrieval and generation tasks. |
Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation (2026.acl-long)
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| Challenge: | Existing LLMs require users to submit raw text regardless of its sensitivity, resulting in substantial computational overhead and degrade model performance. |
| Approach: | They propose a new training pipeline that allows a client-side encoder to condition on k-pooled prompt embeddings instead of raw text and a server-side projection module to fine-tune the projection module and LLM on private, domain-specific data using noise-injected embeddables. |
| Outcome: | The proposed approach eliminates the need for transmitting raw prompt text while maintaining a favorable balance between privacy preservation and model utility for both clients and service providers. |