| 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. |
Similar Papers
Sketch-Driven Regular Expression Generation from Natural Language and Examples (2020.tacl-1)
Copied to clipboard
| 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. |
SoftRegex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence (D19-1)
Copied to clipboard
| 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. |
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (D18-1)
Copied to clipboard
| 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. |
Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
Copied to clipboard
| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
| Outcome: | This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation. |
Query4Regex: Verifiable Regex Transformation through Formal Operations from NL and DSL Queries (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) generate structured data, but their ability to precisely manipulate it remains relatively under-explored. |
| Approach: | They propose a benchmark to evaluate verifiable transformations on regexes . they use natural language instructions and a program-like domain-specific language that specifies the sequence of operations to evaluate LLMs. |
| Outcome: | The proposed benchmark compares LLM performance on natural language and DSL queries for regex manipulation. |
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers (2022.lrec-1)
Copied to clipboard
| Challenge: | resurgence of multimodal datasets has attracted significant research interest, but there is no comprehensive survey for this task. |
| Approach: | They present a survey of a multimodal dataset with different modalities according to the applications. |
| Outcome: | The proposed datasets are available online and discuss the new frontier and motivate future researches. |
The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
| Approach: | They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful. |
| Outcome: | The proposed corpus is searchable through a couple of well-established corpus infrastructures. |
mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data (2025.findings-acl)
Copied to clipboard
| Challenge: | Multimodal embedding models encode multimedia inputs into latent vector representations. |
| Approach: | They propose to synthesize multimodal multilingual data using a multimodal large language model . they identify three criteria for high-quality synthetic multimodal data . |
| Outcome: | The proposed model outperforms existing models on the MMEB Benchmark and the XTD benchmark. |
A Simple Recipe for Multilingual Grammatical Error Correction (2021.acl-short)
Copied to clipboard
| Challenge: | Modern approaches view the task of Grammatical Error Correction (GEC) as monolingual text-to-text rewriting and employ encoderdecoder neural architectures. |
| Approach: | They propose a language-agnostic method to generate a large number of synthetic examples and use large-scale multilingual language models to train state-of-the-art GEC models. |
| Outcome: | The proposed method surpasses state-of-the-art results on GEC benchmarks in English, Czech, German and Russian. |
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (D18-2)
Copied to clipboard
| Challenge: | 77 submissions were received for the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4 of the 73 valid submissions received were either invalid or withdrawn by the authors. |
| Approach: | The volume contains papers from the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4 of the 77 submissions were either invalid or withdrawn by the authors. |
| Outcome: | The system demonstrations session included papers from the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4 of the 73 valid submissions were either invalid or withdrawn by the authors. |