Papers by Yong Suk Choi
Graph Based Network with Contextualized Representations of Turns in Dialogue (2021.emnlp-main)
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| Challenge: | Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. |
| Approach: | They propose a dialogue-based relation extraction model which is based on emotion recognition in conversations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on most of the benchmark datasets. |
Balancing Lexical and Semantic Quality in Abstractive Summarization (2023.acl-short)
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| Challenge: | Existing methods to reduce exposure bias in sequence-to-sequence models are underexplored. |
| Approach: | They propose a method to re-rank sequence-to-sequence neural models to reduce exposure bias. |
| Outcome: | The proposed method achieves an 89.67 BERTScore on the CNN/DailyMail and XSum datasets. |
TH-RAG : Topic-Based Hierarchical Knowledge Graphs for Robust Multi-hop Reasoning in Graph-based RAG Systems (2026.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) enables large language models to incorporate external knowledge at inference. |
| Approach: | They propose a hierarchical framework that organizes triplets into subtopics and topics to enhance connectivity and integrate dispersed information. |
| Outcome: | Experiments on abstractive and specific QA benchmarks show that TH-RAG outperforms strong baselines in accuracy and robustness while remaining efficient. |
XDetox: Text Detoxification with Token-Level Toxicity Explanations (2024.emnlp-main)
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| Challenge: | Existing methods for mitigating toxic content are black-box approaches, which results in limitations in modifying toxic tokens. |
| Approach: | They propose a method that integrates token-level toxicity explanations with the masking and infilling detoxification processes. |
| Outcome: | The proposed method outperforms baseline methods in fluency and toxicity reduction. |
ReGraphRAG: Reorganizing Fragmented Knowledge Graphs for Multi-Perspective Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Graph-based RAG systems have been promising for enabling multi-hop reasoning . but when knowledge graphs are constructed from unstructured documents, they suffer from fragmentation . |
| Approach: | They propose a framework to reconstruct and enrich fragmented knowledge graphs . they propose three core components: Graph Reorganization, Perspective Expansion, and Query-aware Reranking. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on four benchmarks . it achieves over 80% diversity win rate and enables multi-hop reasoning . |
Is Prompt Transfer Always Effective? An Empirical Study of Prompt Transfer for Question Answering (2024.naacl-short)
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| Challenge: | Prompt tuning is an efficient method for initializing pre-trained models . but initialization of prompts is sensitive when the model size is small . |
| Approach: | They propose a method to measure catastrophic forgetting by analyzing prompts for the first time . they characterize a question answering task based on answer format and prompt initialization . |
| Outcome: | The proposed approach can help deepen understanding of prompt tuning. |
COMPEL: Compensated Mixture-of-Experts Pruning with Expert-Layer distribution (2026.findings-acl)
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| Challenge: | Mixture-of-Experts (MoE) architectures are effective for scaling Large Language Models (LLMs) however, existing pruning methods adopt uniform pruning across layers, which fails to capture layer-wise variations in expert importance and redundancy. |
| Approach: | They propose a Mixture-of-Experts pruning method that activates only a subset of experts during inference by estimating expert importance using Fisher information. |
| Outcome: | The proposed pruning method outperforms existing pruning methods while reducing inference latency and peak GPU memory usage. |