The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing
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.
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@inproceedings{DBLP:conf/acl/BanerjeeNUB23,
author = {Debayan Banerjee and
Pranav Ajit Nair and
Ricardo Usbeck and
Chris Biemann},
editor = {Anna Rogers and
Jordan L. Boyd{-}Graber and
Naoaki Okazaki},
title = {The Role of Output Vocabulary in {T2T} LMs for {SPARQL} Semantic Parsing},
booktitle = {Findings of the Association for Computational Linguistics: {ACL} 2023,
Toronto, Canada, July 9-14, 2023},
series = {Findings of {ACL}},
volume = {{ACL} 2023},
pages = {12219--12228},
publisher = {Association for Computational Linguistics},
year = {2023},
url = {https://doi.org/10.18653/v1/2023.findings-acl.774},
doi = {10.18653/V1/2023.FINDINGS-ACL.774},
timestamp = {Tue, 27 Jan 2026 20:26:47 +0100},
biburl = {https://dblp.org/rec/conf/acl/BanerjeeNUB23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}