The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing
Debayan Banerjee, Pranav Nair, Ricardo Usbeck, Chris Biemann
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.
BibTeX
@inproceedings{banerjee-etal-2023-role,
title = "The Role of Output Vocabulary in {T}2{T} {LM}s for {SPARQL} Semantic Parsing",
author = "Banerjee, Debayan and
Nair, Pranav and
Usbeck, Ricardo and
Biemann, Chris",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.774/",
doi = "10.18653/v1/2023.findings-acl.774",
pages = "12219--12228"
}