Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
| Autor: | Regina Barzilay, Eduardo DeLeon, Nicholas Locascio, Karthik Narasimhan, Nate Kushman |
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| Přispěvatelé: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer science business.industry Computer Science - Artificial Intelligence Contrast (statistics) 020207 software engineering 02 engineering and technology computer.software_genre Task (project management) Artificial Intelligence (cs.AI) 0202 electrical engineering electronic engineering information engineering Domain knowledge 020201 artificial intelligence & image processing Regular expression Artificial intelligence business Computation and Language (cs.CL) computer Natural language Natural language processing Meaning (linguistics) |
| Zdroj: | EMNLP arXiv |
| Popis: | This paper explores the task of translating natural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does not utilize domain-specific crafting, learning to translate directly from a parallel corpus. To fully explore the potential of neural models, we propose a methodology for collecting a large corpus of regular expression, natural language pairs. Our resulting model achieves a performance gain of 19.6% over previous state-of-the-art models. to be published in EMNLP 2016 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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