AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL. (arXiv:2208.12461v1 [cs.CL])

This study investigates the task of knowledge-based question generation
(KBQG). Conventional KBQG works generated questions from fact triples in the
knowledge graph, which could not express complex operations like aggregation
and comparison in SPARQL. Moreover, due to the costly annotation of large-scale
SPARQL-question pairs, KBQG from SPARQL under low-resource scenarios urgently
needs to be explored. Recently, since the generative pre-trained language
models (PLMs) typically trained in natural language (NL)-to-NL paradigm have
been proven effective for low-resource generation, e.g., T5 and …

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