Papers by Sang-Ki Ko
GDA: Grammar-based Data Augmentation for Text Classification using Slot Information (2023.findings-emnlp)
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| Challenge: | Recent studies suggest data augmentation approaches to resolve the low-resource problem in natural language processing tasks. |
| Approach: | They propose to use slot information to augment sentences using a set of injective relations between a sentence’s semantics and its syntactical structure to augment the dataset. |
| Outcome: | The proposed approach outperforms all other data augmentation methods by 19.38%. |
SoftRegex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence (D19-1)
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| Challenge: | Existing models that generate semantically correct regular expressions from NLs are not yet fully understood. |
| Approach: | They propose a model that rewards reinforcement learning based on the semantic equivalence between two regular expressions. |
| Outcome: | The proposed model reduces training time and produces state-of-the-art results on three benchmark datasets. |
ContractEval: A Benchmark for Evaluating Contract-Satisfying Assertions in Code Generation (2026.findings-acl)
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| Challenge: | Current evaluations measure functional correctness on well-formed inputs, but they filter out inputs that violate them. |
| Approach: | They propose a benchmark to evaluate whether generated code enforces preconditions . they use a neuro-symbolic pipeline to evaluate code with test cases . |
| Outcome: | The proposed benchmark aims to evaluate whether generated code enforces preconditions . it aims at achieving pass@k scores while ignoring those that violate them . |
MultiFix: Learning to Repair Multiple Errors by Optimal Alignment Learning (2021.findings-emnlp)
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| Challenge: | Existing approaches fix a single error in a line, but it is inevitable to iterate until no errors remain. |
| Approach: | They propose a sequence-to-sequence learning framework for fixing multiple program errors at once . they pare an erroneous program with an optimal alignment to the correct program . |
| Outcome: | The proposed approach achieves state-of-the-art on a dataset of 6,975 erroneous C programs . the proposed framework is based on an edit-distance-based data labeling approach . |
CodeComplex: Dataset for Worst-Case Time Complexity Prediction (2025.findings-emnlp)
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| Challenge: | Reasoning ability of large language models (LLMs) is crucial in complex decision-making tasks. |
| Approach: | They propose to use code time complexity prediction to assess LLMs' reasoning ability. |
| Outcome: | The proposed dataset comprises 4,900 Java codes and an equivalent number of Python codes. |