  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.
