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        <title>Semantic Parsing for Conversational Question Answering over Knowledge Graphs</title>
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    <name type="personal">
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    <name type="personal">
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    <abstract>In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.</abstract>
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%0 Conference Proceedings
%T Semantic Parsing for Conversational Question Answering over Knowledge Graphs
%A Perez-Beltrachini, Laura
%A Jain, Parag
%A Monti, Emilio
%A Lapata, Mirella
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F perez-beltrachini-etal-2023-semantic
%X In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.
%R 10.18653/v1/2023.eacl-main.184
%U https://aclanthology.org/2023.eacl-main.184
%U https://doi.org/10.18653/v1/2023.eacl-main.184
%P 2507-2522

Markdown (Informal)

[Semantic Parsing for Conversational Question Answering over Knowledge Graphs](https://aclanthology.org/2023.eacl-main.184) (Perez-Beltrachini et al., EACL 2023)

• Semantic Parsing for Conversational Question Answering over Knowledge Graphs (Perez-Beltrachini et al., EACL 2023)

ACL

• Laura Perez-Beltrachini, Parag Jain, Emilio Monti, and Mirella Lapata. 2023. Semantic Parsing for Conversational Question Answering over Knowledge Graphs. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2507–2522, Dubrovnik, Croatia. Association for Computational Linguistics.