Papers with rule-based systems
Discourse-Aware In-Context Learning for Temporal Expression Normalization (2024.naacl-short)
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| Challenge: | Temporal expression (TE) normalization is a well-studied problem, but upcoming machine learning approaches suffer from a lack of labeled data. |
| Approach: | They propose to use in-context learning to inject task, document, and example information into a large language model for temporal expression normalization. |
| Outcome: | The proposed model performs better in non-standard settings by dynamically including relevant examples during inference. |
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT (2020.emnlp-main)
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| Challenge: | Existing approaches to label radiology text reports rely on feature engineering based on medical domain knowledge or manual annotations by experts. |
| Approach: | They propose a BERT-based approach to medical image report labeling that exploits the scale of available rule-based systems and the quality of expert annotations. |
| Outcome: | The proposed model outperforms the previous best rules-based labeler with statistical significance on one of the largest datasets of chest x-rays. |
Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)
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| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has been effective on structured tasks, but its reliance on simple, rule-based verifiers creates a bottleneck. |
| Approach: | They propose a framework that uses a generative verifier to provide soft, probabilistic rewards. |
| Outcome: | The proposed framework outperforms existing models up to 10x their size and can be scalable and effective. |
Are You for Real? Detecting Identity Fraud via Dialogue Interactions (D19-1)
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| Challenge: | Existing methods to detect identity fraud are prone to errors and are not based on real data. |
| Approach: | They propose to use a KG constructor and structured dialogue management to detect identity fraud in loan applications to generate questions based on personal information. |
| Outcome: | The proposed system can detect fraudsters and achieve higher recognition accuracy compared with rule-based systems. |
Repairing Regex Vulnerabilities via Localization-Guided Instructions (2026.eacl-long)
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| Challenge: | Current approaches to regular expression denial of service (ReDoS) are hampered by a trade-off. |
| Approach: | They propose a framework to harness LLM generalization while enforcing reliability by localizing the vulnerable subpattern and generating a semanticallyequivalent fix for this isolated segment. |
| Outcome: | The proposed framework improves repair rates by 15.4%p over the state-of-the-art. |
Non-neural Models Matter: a Re-evaluation of Neural Referring Expression Generation Systems (2022.acl-long)
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| Challenge: | In recent years, neural models have outperformed rule-based and classic approaches in NLG. |
| Approach: | They evaluate two English datasets and evaluate their performance using automatic and human evaluations. |
| Outcome: | The proposed model outperforms rule-based and classic approaches on two English datasets and is compared with human-based models. |
Is this chart lying to me? Automating the detection of misleading visualizations (2026.acl-long)
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| Challenge: | Prior work has shown that both humans and MLLMs are frequently deceived by misleading visualizations. |
| Approach: | They propose a benchmark of 2,604 real-world visualizations annotated with 12 types of misleaders. |
| Outcome: | The proposed framework can detect misleading visualizations and identify specific design rules they violate . the proposed framework is based on a synthetic dataset of 81,814 visualizations . |
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks (D18-1)
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| Challenge: | Existing rule-based question generation models rely on one or two sentences as input, while long text has posed challenges for sequence to sequence neural models. |
| Approach: | They propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation. |
| Outcome: | The proposed model outperforms existing models with sentence-level or paragraph-level inputs pushing the state-of-the-art result from 13.9 to 16.3 (BLEU_4). |
Help! Need Advice on Identifying Advice (2020.emnlp-main)
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| Challenge: | Pre-trained systems are able to capture advice better than rule-based systems, but advice identification is challenging. |
| Approach: | They analyze a dataset of advice posts on two reddit forums and annotate whether they contain advice. |
| Outcome: | The proposed models show that pre-trained models capture advice better than rule-based systems, but advice identification is challenging. |