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.

翻译：本研究分析了文本到文本（T2T）模型在SPARQL语义解析任务中输出词汇的作用。我们在知识图谱问答（KGQA）背景下开展实验，该任务旨在将自然语言问题转换为SPARQL查询语言。我们观察到查询词汇与人类词汇存在显著差异。由于语言模型（LM）主要针对人类语言任务进行训练，若将查询词汇替换为更适配LM分词器的词汇，模型性能可能得到提升。通过对查询进行精心选择的词汇替换，我们在GrailQA数据集上获得了最高17%的绝对性能提升。