AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL
Guanming Xiong, Junwei Bao, Wen Zhao, Youzheng Wu, Xiaodong He

TL;DR
AutoQGS introduces an auto-prompt method that enables low-resource knowledge-based question generation from SPARQL queries by directly generating questions and rephrasing SPARQL into natural language descriptions, achieving state-of-the-art results.
Contribution
The paper presents a novel auto-prompt approach that effectively generates questions from SPARQL in low-resource scenarios, handling complex operations and rephrasing SPARQL into natural language.
Findings
Achieves state-of-the-art performance on benchmark datasets.
Effectively handles complex SPARQL operations.
Generates a large corpus of question-SPARQL pairs for future research.
Abstract
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 BART, how to effectively utilize them to generate NL-question from non-NL SPARQL is challenging. To address these challenges, AutoQGS, an auto-prompt approach for low-resource KBQG from SPARQL, is proposed. Firstly, we put forward to generate questions directly from SPARQL for the KBQG task to…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
MethodsAttention Is All You Need · Linear Layer · Softmax · Layer Normalization · SentencePiece · Adam · Byte Pair Encoding · Gated Linear Unit · Dense Connections · Inverse Square Root Schedule
