Papers by Hyunsouk Cho

5 papers
Self-Supervised Multimodal Opinion Summarization (2021.acl-long)

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Challenge: Existing methods for opinion summarization use text data, but non-text data are less abundant.
Approach: They propose a self-supervised opinion summarization framework that uses non-text data to generate a summary from multiple reviews.
Outcome: The proposed framework is superior to existing methods on Yelp and Amazon datasets.
Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning (L18-1)

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Challenge: Existing methods for evaluating plausibility of events are focused on measuring causal dependency between events or actions.
Approach: They propose a task to identify the more plausible alternative with their commonsense causal context.
Outcome: The proposed task is based on a visual COPA dataset with 380 questions and over 1K images with various topics.
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0 (2020.lrec-1)

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Challenge: Existing models are brittle for adversarial perturbed questions, causing uncertainty . a dataset with annotations on unanswerable questions is not available to solve this problem .
Approach: They use crowdsourced annotations to annotate unanswerable questions . they also annotated which part of the question causes unanswered questions a .
Outcome: The proposed dataset can be used to improve model interpretation, authors say . they find that existing models are brittle for adversarial perturbed questions .
FLEX: Expert-level False-Less EXecution Metric for Text-to-SQL Benchmark (2025.naacl-long)

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Challenge: Existing evaluation methods for text-to-SQL systems show many false positives and negatives . however, the Execution Accuracy (EX) metric is flawed and can diverge from human experts.
Approach: They propose a method to evaluate text-to-SQL systems using large language models to emulate human expert-level evaluation of SQL queries.
Outcome: The proposed metric improves agreement with human experts with comprehensive context and sophisticated criteria.
GTA: Gated Toxicity Avoidance for LM Performance Preservation (2023.findings-emnlp)

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Challenge: Existing Controllable Text Generation methods that generate toxic text can negatively impact the performance of the language model.
Approach: They propose a gated Toxicity Avoidance method that can be applied to any CTG method and evaluate its effectiveness.
Outcome: The proposed method reduces toxicity and preserves performance while preserving the language model's generation performance.

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