Marais, Laurette2021-12-172021-12-172021-09Marais, L. 2021. Approximating a Zulu GF concrete syntax with a neural network for natural language understanding. http://hdl.handle.net/10204/12202 .http://hdl.handle.net/10204/12202Multilingual Grammatical Framework (GF) domain grammars have been used in a variety of different applications, including question answering, where concrete syntaxes for parsing questions and generating answers are typically required for each supported language. In low-resourced settings, grammar engineering skills, appropriate knowledge of the use of supported languages in a domain, and appropriate domain data are scarce. This presents a challenge for developing domain specific concrete syntaxes for a GF application grammar, on the one hand, while on the other hand, machine learning techniques for performing question answering are hampered by a lack of sufficient data. This paper presents a method for overcoming the two challenges of scarce or costly grammar engineering skills and lack of data for machine learning. A Zulu resource grammar is leveraged to create sufficient data to train a neural network that approximates a Zulu concrete syntax for parsing questions in a proof-of-concept question-answering system.FulltextenGrammatical frameworksGFZulu resource grammarData augmentationControlled natural languagesConference PresentationMarais, L. (2021). Approximating a Zulu GF concrete syntax with a neural network for natural language understanding. http://hdl.handle.net/10204/12202Marais, Laurette. "Approximating a Zulu GF concrete syntax with a neural network for natural language understanding." <i>Proceedings of the Seventh International Workshop on Controlled Natural Language, Amsterdam, Netherlands, 8-9 September 2021</i> (2021): http://hdl.handle.net/10204/12202Marais L, Approximating a Zulu GF concrete syntax with a neural network for natural language understanding; 2021. http://hdl.handle.net/10204/12202 .TY - Conference Presentation AU - Marais, Laurette AB - Multilingual Grammatical Framework (GF) domain grammars have been used in a variety of different applications, including question answering, where concrete syntaxes for parsing questions and generating answers are typically required for each supported language. In low-resourced settings, grammar engineering skills, appropriate knowledge of the use of supported languages in a domain, and appropriate domain data are scarce. This presents a challenge for developing domain specific concrete syntaxes for a GF application grammar, on the one hand, while on the other hand, machine learning techniques for performing question answering are hampered by a lack of sufficient data. This paper presents a method for overcoming the two challenges of scarce or costly grammar engineering skills and lack of data for machine learning. A Zulu resource grammar is leveraged to create sufficient data to train a neural network that approximates a Zulu concrete syntax for parsing questions in a proof-of-concept question-answering system. DA - 2021-09 DB - ResearchSpace DP - CSIR J1 - Proceedings of the Seventh International Workshop on Controlled Natural Language, Amsterdam, Netherlands, 8-9 September 2021 KW - Grammatical frameworks KW - GF KW - Zulu resource grammar KW - Data augmentation KW - Controlled natural languages LK - https://researchspace.csir.co.za PY - 2021 T1 - Approximating a Zulu GF concrete syntax with a neural network for natural language understanding TI - Approximating a Zulu GF concrete syntax with a neural network for natural language understanding UR - http://hdl.handle.net/10204/12202 ER -25164