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        <title>The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing</title>
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    <abstract>In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing. We perform experiments within the the context of knowledge graph question answering (KGQA), where the task is to convert questions in natural language to the SPARQL query language. We observe that the query vocabulary is distinct from human vocabulary. Language Models (LMs) are pre-dominantly trained for human language tasks, and hence, if the query vocabulary is replaced with a vocabulary more attuned to the LM tokenizer, the performance of models may improve. We carry out carefully selected vocabulary substitutions on the queries and find absolute gains in the range of 17% on the GrailQA dataset.</abstract>
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%0 Conference Proceedings
%T The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing
%A Banerjee, Debayan
%A Nair, Pranav
%A Usbeck, Ricardo
%A Biemann, Chris
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F banerjee-etal-2023-role
%X In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing. We perform experiments within the the context of knowledge graph question answering (KGQA), where the task is to convert questions in natural language to the SPARQL query language. We observe that the query vocabulary is distinct from human vocabulary. Language Models (LMs) are pre-dominantly trained for human language tasks, and hence, if the query vocabulary is replaced with a vocabulary more attuned to the LM tokenizer, the performance of models may improve. We carry out carefully selected vocabulary substitutions on the queries and find absolute gains in the range of 17% on the GrailQA dataset.
%R 10.18653/v1/2023.findings-acl.774
%U https://aclanthology.org/2023.findings-acl.774
%U https://doi.org/10.18653/v1/2023.findings-acl.774
%P 12219-12228

Markdown (Informal)

[The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing](https://aclanthology.org/2023.findings-acl.774) (Banerjee et al., Findings 2023)

• The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing (Banerjee et al., Findings 2023)

ACL

• Debayan Banerjee, Pranav Nair, Ricardo Usbeck, and Chris Biemann. 2023. The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12219–12228, Toronto, Canada. Association for Computational Linguistics.