Papers with APR
PunKtuator: A Multilingual Punctuation Restoration System for Spoken and Written Text (2021.eacl-demos)
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| Challenge: | Prior punctuation restoration methods have focused on using lexical features, prosodic features or combination of both. |
| Approach: | They propose a multitask modeling approach to restore punctuation in multiple high resource languages using acoustic models and a computational model. |
| Outcome: | The proposed system can restore punctuation in Germanic, Romanic and low resource languages without extensive knowledge of grammar or syntax. |
Grammar-Based Patches Generation for Automated Program Repair (2021.findings-acl)
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| Challenge: | Automated program repair (APR) aims to find an automatic solution to program language bugs without human intervention. |
| Approach: | They propose a grammar-based rule-rule model which regards the repair process as the transformation of grammar rules and employs a tree-based self-attention approach to guarantee grammar correctness. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a Java dataset in terms of generated code accuracy. |
Towards Low-Resource Automatic Program Repair with Meta-Learning and Pretrained Language Models (2023.emnlp-main)
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| Challenge: | Recent advances in deep learning (DL) based APR models have demonstrated promising results by learning from large-scale bug-fix examples in a data-driven manner. |
| Approach: | They propose a meta-learning framework integrated with code pretrained language models to generate fixes for low-resource bugs with limited training samples. |
| Outcome: | The proposed framework learns better error-specific knowledge from high-resource bugs through efficient first-order meta-learning optimization, which allows for a faster adaptation to the target low-resourced bugs. |
Mitigating Legal Hallucinations via Symbolic Constraints and Analogical Precedents (2026.acl-long)
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| Challenge: | Existing methods for finetuning and retrieval-augmented generation suffer from hallucination risk and semantic drift. |
| Approach: | They propose a framework for a dual-retriever based on the legal syllogism and the nature of different legal data. |
| Outcome: | The proposed framework mitigates hallucinations while improving explainability of legal reasoning. |
RePair: Automated Program Repair with Process-based Feedback (2024.findings-acl)
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| Challenge: | Commercial-scale language models (LMs) have taken APR to unprecedented levels, but they are limited by parameters and humans interact with them through explicit prompts. |
| Approach: | They propose a method that utilizes process supervision to improve program repair by allowing users to input feedback from compilers and test cases. |
| Outcome: | The proposed method outperforms large outcome-based generation methods and is inspired by strategies used in programming competitions. |
CascadeFix: Multi-Location Program Repair via Cascading Planning and Generation (2026.findings-acl)
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| Challenge: | Existing methods for automating program repair face insufficient bug dependency modeling and inadequate global repair planning when addressing semantically complex multi-location bugs. |
| Approach: | They propose a multi-location automatic repair method via cascading planning and generation . they propose to model dependencies among bugs and cluster them to ensure rationality . |
| Outcome: | The proposed method resolves 84 multi-location bugs, achieving a 31% improvement over current methods. |