Papers by Hojun Cho
Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation (2025.naacl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a challenging task of extracting sentiments along with their corresponding aspects and opinion terms from text. |
| Approach: | They propose a pipeline that expands existing evaluation sets by adding alternative valid terms for aspect and opinion. |
| Outcome: | The proposed evaluation set uncovers the capabilities of large language models (LLMs) in ABSA tasks, which is concealed by the single-answer GT sets. |
Talk to Your Slides: High-Efficiency Slide Editing via Language-Driven Structured Data Manipulation (2026.findings-acl)
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| Challenge: | Talk-to-Your-Slides is a high-efficiency slide editing agent that uses language-driven structured data manipulation instead of the image modality. |
| Approach: | They propose a language-driven slide editing agent that uses language-based structured data manipulation instead of image modality. |
| Outcome: | The proposed system achieves faster processing and better instruction fidelity than GUI-based agents. |
Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts (2025.findings-emnlp)
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| Challenge: | Using a dataset of Korean weather queries, we find that automatic speech recognition systems fail on specialized vocabulary. |
| Approach: | They propose an evaluation dataset of Korean weather queries . the dataset was recorded by diverse native speakers following pronunciation guidelines . |
| Outcome: | The proposed model reduces error rates on meteorological terms and improves overall recognition accuracy. |
Rethinking Style Transformer with Energy-based Interpretation: Adversarial Unsupervised Style Transfer using a Pretrained Model (2022.emnlp-main)
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Hojun Cho, Dohee Kim, Seungwoo Ryu, ChaeHun Park, Hyungjong Noh, Jeong-in Hwang, Minseok Choi, Edward Choi, Jaegul Choo
| Challenge: | Existing methods to train text style transfer models with adversarial loss degrade fluency compared to other metrics. |
| Approach: | They propose a method which leverages a pretrained language model to improve fluency by restructuring the discriminator and the model itself. |
| Outcome: | The proposed model achieves state-of-the-art on three public benchmarks and achieved state-outperformance on the overall metrics. |
Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information (2025.emnlp-industry)
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| Challenge: | Existing language agents powered by large language models face resource-constrained environments . proprietary models raise concerns in cost and service dependency, while large-scale open-source models require substantial computational resources. |
| Approach: | They propose a Korean chemical toxicity information agent that reduces token consumption . they propose 'scenario-based dialogue generation' methodology that distills tool-using capabilities from larger models. |
| Outcome: | The proposed language agent outperforms untuned models and baseline approaches in DB faithfulness and preference. |