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. |
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SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (D18-1)
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| Challenge: | Existing approaches to generate programs from natural language do not address program aliasing . semantically equivalent programs may have many syntactically different forms . |
| Approach: | They propose a semantics-based approach to generate regular expressions from natural language. |
| Outcome: | The proposed approach improves on three public datasets. |
Sketch-Driven Regular Expression Generation from Natural Language and Examples (2020.tacl-1)
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| Challenge: | Recent systems for converting natural language descriptions into regexes have achieved some success, but typically deal with short, formulaic text and can only produce simple regexe. |
| Approach: | They propose a framework for regex synthesis in a context where both natural language and examples are available. |
| Outcome: | The proposed framework achieves state-of-the-art on two prior datasets and a real-world dataset, which existing neural systems completely fail on. |
Benchmarking Multimodal Regex Synthesis with Complex Structures (2020.acl-main)
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| Challenge: | Existing datasets for regex generation from natural language are limited in complexity . Existing regex synthesis datasets are simple and the language used to describe them is not diverse . |
| Approach: | They propose a dataset for regex generation from natural language that generates regexes using a probabilistic grammar and pre-defined macros. |
| Outcome: | The proposed dataset is compared to existing datasets for regex generation from natural language . it generates the regexes using a probabilistic grammar with pre-defined macros observed from real-world StackOverflow posts. |
A Regex Minimization Benchmark: A PSPACE-Complete Challenge for Language Models (2026.eacl-long)
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| Challenge: | Language models (LMs) have demonstrated impressive reasoning capabilities across domains . but their ability to handle PSPACE-complete problems remains underexplored . a new benchmark for regex minimization is proposed to evaluate LMs' reasoning capabilities . |
| Approach: | They propose a benchmark for regex minimization to evaluate LMs' reasoning power . they use a million regexes paired with their minimal equivalents to evaluate their performance . |
| Outcome: | The proposed model can solve NP-complete problems, but their ability to handle PSPACE-complete ones remains underexplored. |
Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)
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| Challenge: | Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available. |
| Approach: | They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN. |
| Outcome: | The proposed approach significantly improves learning effectiveness when a small number of training examples are available. |
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (P19-1)
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| Challenge: | Recent neural language generation systems often hallucinate contents when trained on loosely corresponding pairs of the input structure and text. |
| Approach: | They propose to integrate a language understanding module for data refinement with self-training iterations to induce strong equivalence between the input data and the paired text. |
| Outcome: | Experiments on the E2E challenge dataset show that the proposed framework reduces relative unaligned noise by 50% compared with the current state-of-the-art ensemble generator. |
Enhancing Language Model with Unit Test Techniques for Efficient Regular Expression Generation (2023.emnlp-industry)
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| Challenge: | Recent studies have shown that generative language models lack functional correctness, which is a critical aspect of regular expressions. |
| Approach: | They propose a method that takes functional correctness into account and transforms it into a differentiable gradient feedback using policy gradient techniques. |
| Outcome: | The proposed method has been used in a regulatory scenario to ensure that all online content is free from non-compliant elements, thereby significantly reducing the workload of relevant personnel. |
Speeding Up Entmax (2022.findings-naacl)
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| Challenge: | Recent studies suggest that sparsity is a problem when the trained model is used for inference. |
| Approach: | They propose an alternative to softmax that produces a dense probability distribution but is slower than softmax. |
| Outcome: | The proposed method keeps its virtuous characteristics but is slower than softmax and achieves on par or better performance in machine translation task. |
NeuralREG: An end-to-end approach to referring expression generation (P18-1)
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| Challenge: | Referring Expression Generation models typically rely on features such as salience and grammatical function to make decisions about form and content. |
| Approach: | They propose a new approach that makes decisions about form and content in one go . they use a delexicalized version of the WebNLG corpus to test the approach . |
| Outcome: | The proposed approach significantly improves over two strong baselines. |
Soft Syntactic Reinforcement for Neural Event Extraction (2025.naacl-long)
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| Challenge: | Recent event extraction methods rely on pre-trained language models but still suffer from errors due to a lack of syntactic knowledge. |
| Approach: | They propose a method to incorporate syntactic information into PLM-based models for event extraction (EE) this method uses a standard dependency corpus to select syntax-related dimensions of the model's representation. |
| Outcome: | The proposed method outperforms baseline models and existing syntactic reinforcement methods on sentence-level and document-level EE benchmark datasets. |