从自然语言文本合成LTL公式:现状与研究方向

Time Pub Date : 2019-10-01 DOI:10.4230/LIPIcs.TIME.2019.17
Andrea Brunello, A. Montanari, M. Reynolds
{"title":"从自然语言文本合成LTL公式:现状与研究方向","authors":"Andrea Brunello, A. Montanari, M. Reynolds","doi":"10.4230/LIPIcs.TIME.2019.17","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":75226,"journal":{"name":"Time","volume":"1 1","pages":"17:1-17:19"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":"{\"title\":\"Synthesis of LTL Formulas from Natural Language Texts: State of the Art and Research Directions\",\"authors\":\"Andrea Brunello, A. Montanari, M. Reynolds\",\"doi\":\"10.4230/LIPIcs.TIME.2019.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":75226,\"journal\":{\"name\":\"Time\",\"volume\":\"1 1\",\"pages\":\"17:1-17:19\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"31\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Time\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4230/LIPIcs.TIME.2019.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Time","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4230/LIPIcs.TIME.2019.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 31

摘要

线性时间逻辑(LTL)常用于模型检查任务;此外,它非常适合技术需求的形式化。然而,时间逻辑公式的正确规范和解释需要强大的数学背景,很难由领域专家完成,相反,他们倾向于依赖于预期系统行为的自然语言描述。在这种情况下,一个能够自动将英语句子翻译成LTL公式,反之亦然的系统将大有帮助。虽然通过正确解析公式,可以以一种相对简单的方式将LTL公式呈现为更可读的英语句子,但由于解释自由的自然语言文本的固有困难,相反的任务仍然是一个开放的问题。虽然过去已经提出了几个部分的解决方案,但文献仍然缺乏对所做工作的批判性评估。我们解决了这一缺点,介绍了英语到ltl翻译问题的现状,并概述了一些可能的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Synthesis of LTL Formulas from Natural Language Texts: State of the Art and Research Directions
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Early Detection of Temporal Constraint Violations LSCPM: communities in massive real-world Link Streams by Clique Percolation Method Taming Strategy Logic: Non-Recurrent Fragments Realizability Problem for Constraint LTL Logical Forms of Chronicles
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1