Papers by Heesoo Park

5 papers
MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation (2025.coling-main)

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Challenge: Recent advances in large language models have enabled in-context learning (ICL)-based methods to outperform fine-tuning approaches for text-to-SQL tasks.
Approach: They propose a method that leverages multiple prompts to explore a broader search space for possible answers and effectively aggregate them.
Outcome: The proposed method achieves execution accuracies of 65.5% and 89.6% on BIRD and Spider benchmarks.
Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning (2024.findings-acl)

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Challenge: Existing methods rely on linear sequential operations to solve First-Order Logic queries.
Approach: They propose a model-agnostic approach that fully integrates the context of the query graph.
Outcome: The proposed method improves performance on two datasets by 19.5%.
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories (2021.naacl-main)

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Challenge: Existing studies have developed computational models to recognize metaphorical words in sentences.
Approach: They propose a model that leverages contextualized word representation and linguistic metaphor identification theories to detect whether the target word is metaphorical.
Outcome: The proposed model outperforms baseline models on four benchmark datasets . it leverages contextualized word representation and linguistic metaphor identification theories to detect whether the target word is metaphorical.
Enhancing Complex Reasoning in Knowledge Graph Question Answering through Query Graph Approximation (2025.findings-acl)

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Challenge: Existing knowledge-grounded question answering frameworks lack essential triplets related to the questions . Existing approaches to knowledge-based QA are incomplete in the context of KGs .
Approach: They propose a framework to provide answers to structured queries by leveraging Knowledge Graphs.
Outcome: The proposed framework outperforms existing methods on QA tasks where KGs are incomplete . the framework is based on a set of data from a dataset of QA questions .
IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion (2021.acl-demo)

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Challenge: Existing computer-aided translation tools require the translator to edit incorrect parts of a document, while ITP tools require fewer edits.
Approach: They propose an interactive translation interface with neural models that streamline the post-editing process on machine translation output.
Outcome: The proposed interface can significantly improve translation quality and a user study shows that it speeds up the post-editing process by 52.9% compared to translating from scratch.

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