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  • 标题:Synthesis of LTL Formulas from Natural Language Texts: State of the Art and Research Directions
  • 本地全文:下载
  • 作者:Andrea Brunello ; Angelo Montanari ; Mark Reynolds
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2019
  • 卷号:147
  • 页码:1-19
  • DOI:10.4230/LIPIcs.TIME.2019.17
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:Linear temporal logic (LTL) is commonly used in model checking tasks; moreover, it is well-suited for the formalization of technical requirements. However, the correct specification and interpretation of temporal logic formulas require a strong mathematical background and can hardly be done by domain experts, who, instead, tend to rely on a natural language description of the intended system behaviour. In such situations, a system that is able to automatically translate English sentences into LTL formulas, and vice versa, would be of great help. While the task of rendering an LTL formula into a more readable English sentence may be carried out in a relatively easy way by properly parsing the formula, the converse is still an open problem, due to the inherent difficulty of interpreting free, natural language texts. Although several partial solutions have been proposed in the past, the literature still lacks a critical assessment of the work done. We address such a shortcoming, presenting the current state of the art for what concerns the English-to-LTL translation problem, and outlining some possible research directions.
  • 关键词:Evolutionary algorithms; Machine learning; Natural language processing; Semantic parsing; Temporal logic
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