Human-Robot Interaction in a Shopping Mall: A CNL Approach

Demirel, Ezgi; Gür, Kamil Doruk; Erdem, Esra

doi:10.1007/978-3-319-41498-0_11

“…Answer set programming has been used for syntactic parsing in the context of the Combinatory Categorial Grammar formalism (Lieler andSchüller 2012, Schüller 2013), for semantic parsing and representation of textual information (Baral et al 2011, Nguyen et al 2015, and for natural language understanding in the context of question answering (Baral and Tari 2006) and the Winograd Schema Challenge (Schüller 2014, Bailey et al 2015. In the context of controlled natural language processing, answer set programming has been used as a prototype of a rule system in the Attempto project (Kuhn 2007, Fuchs et al 2008, as a target language for biomedical queries related to drug discovery (Erdem and Yeniterzi 2009), as a source language for generating explanations for biomedical queries (Erdem and Öztok 2015), as a framework for human-robot interaction (Demirel et al 2016), and as a target language for writing executable specifications (Guy and Schwitter 2017).…”

Section: Answer Set Programming and Natural Language Processingmentioning

confidence: 99%

Specifying and Verbalising Answer Set Programs in Controlled Natural Language

Schwitter¹

2018

Theory and Practice of Logic Programming

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We show how a bi-directional grammar can be used to specify and verbalise answer set programs in controlled natural language. We start from a program specification in controlled natural language and translate this specification automatically into an executable answer set program. The resulting answer set program can be modified following certain naming conventions and the revised version of the program can then be verbalised in the same subset of natural language that was used as specification language. The bi-directional grammar is parametrised for processing and generation, deals with referring expressions, and exploits symmetries in the data structure of the grammar rules whenever these grammar rules need to be duplicated. We demonstrate that verbalisation requires sentence planning in order to aggregate similar structures with the aim to improve the readability of the generated specification. Without modifications, the generated specification is always semantically equivalent to the original one; our bi-directional grammar is the first one that allows for semantic round-tripping in the context of controlled natural language processing. This paper is under consideration for acceptance in TPLP.

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“…Answer set programming has been used for syntactic parsing in the context of the Combinatory Categorial Grammar formalism (Lieler andSchüller 2012, Schüller 2013), for semantic parsing and representation of textual information (Baral et al 2011, Nguyen et al 2015, and for natural language understanding in the context of question answering (Tari and Baral 2005) and the Winograd Schema Challenge (Schüller 2014, Bailey et al 2015. In the context of controlled natural language processing, answer set programming has been used as a prototype of a rule system in the Attempto project (Kuhn 2007, Fuchs et al 2008, as a target language for biomedical queries related to drug discovery (Erdem and Yeniterzi 2009), as a source language for generating explanations for biomedical queries (Erdem andÖztok 2015), as a framework for human-robot interaction (Demirel et al 2016), and as a target language for writing executable specifications (Guy and Schwitter 2017).…”

Section: Answer Set Programming and Natural Language Processingmentioning

confidence: 99%

Specifying and Verbalising Answer Set Programs in Controlled Natural Language

Schwitter¹

2018

EasyChair Preprints

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View full text Add to dashboard Cite

We show how a bi-directional grammar can be used to specify and verbalise answer set programs in controlled natural language. We start from a program specification in controlled natural language and translate this specification automatically into an executable answer set program. The resulting answer set program can be modified following certain naming conventions and the revised version of the program can then be verbalised in the same subset of natural language that was used as specification language. The bi-directional grammar is parametrised for processing and generation, deals with referring expressions, and exploits symmetries in the data structure of the grammar rules whenever these grammar rules need to be duplicated. We demonstrate that verbalisation requires sentence planning in order to aggregate similar structures with the aim to improve the readability of the generated specification. Without modifications, the generated specification is always semantically equivalent to the original one; our bi-directional grammar is the first one that allows for semantic round-tripping in the context of controlled natural language processing.

show abstract