The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing

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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.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: ACL 2023 : July 9-14, 2023
EditorsAnna Rogers, Jordan L. Boyd-Graber, Naoaki Okazaki
Number of pages10
Place of PublicationStroudsburg
PublisherAssociation for Computational Linguistics (ACL)
Publication date01.07.2023
Pages12219-12228
ISBN (electronic)978-1-959429-62-3
DOIs
Publication statusPublished - 01.07.2023
Externally publishedYes
Event61st Annual Meeting of the Association for Computational Linguistics - Toronto, Canada
Duration: 09.07.202314.07.2023
Conference number: 61
https://2023.aclweb.org

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