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Hyunsouk Cho

7 accepted papers

2026

TextME: Bridging Unseen Modalities Through Text Descriptions

ICML 2026poster

Expanding multimodal representations to novel modalities is constrained by reliance on large-scale paired datasets (e.g., text–image, text–audio, text–3D, text–molecule), which are costly and often infeasible in domains requiring expert annotation such as medical imaging and molecular analysis. We i…

Cited by 0SourceScholar
2025

FLEX: Expert-level False-Less EXecution Metric for Text-to-SQL Benchmark

NAACL 2025long

Text-to-SQL systems have become crucial for translating natural language into SQL queries in various industries, enabling non-technical users to perform complex data operations. The need for accurate evaluation methods has increased as these systems have grown more sophisticated. However, the Execut…

2024

Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation

ACL 2024long

Emotional Support Conversation (ESC) is a task aimed at alleviating individuals’ emotional distress through daily conversation. Given its inherent complexity and non-intuitive nature, ESConv dataset incorporates support strategies to facilitate the generation of appropriate responses. Recently, desp…

2022

FPAdaMetric: False-Positive-Aware Adaptive Metric Learning for Session-Based Recommendation

AAAI 2022technical

Modern recommendation systems are mostly based on implicit feedback data which can be quite noisy due to false positives (FPs) caused by many reasons, such as misclicks or quick curiosity. Numerous recommendation algorithms based on collaborative filtering have leveraged post-click user behavior (e.…

2021

Self-Supervised Multimodal Opinion Summarization

ACL 2021long

Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related to reviews have been considered less often. To us…