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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 withinthe the context of knowledge graph question answering (KGQA), where the task isto convert questions in natural language to the SPARQL query language. Weobserve that the query vocabulary is distinct from human vocabulary. LanguageModels (LMs) are pre-dominantly trained for human language tasks, and hence, ifthe query vocabulary is replaced with a vocabulary more attuned to the LMtokenizer, the performance of models may improve. We carry out carefullyselected vocabulary substitutions on the queries and find absolute gains in therange of 17% on the GrailQA dataset.

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