2023 GeneratingandAnsweringSimpleand

(Han & Gardent, 2023) ⇒ Kelvin Han, and Claire Gardent. (2023). “Generating and Answering Simple and Complex Questions from Text and from Knowledge Graphs.” In: Proceedings of the The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL 2023).

Subject Headings: QA Model.

Notes

While both text and knowledge graphs (KG) may be used to answer a question, most current question answering and question generation models only work on a single modality. In this paper, we introduce a multi-task model such that questions can be generated and answered from both KG and text. The model has wide coverage and handles both simple (one KG fact) and complex (more than one KG fact) questions. Extensive internal consistency, cross-modal consistency, and external consistency checks, and analysis of the quality of the generated questions, show that our approach outperforms previous work. Our data and modeling also leads to improvements in downstream tasks, including better performance with fine-tuning Open-Domain QA architectures and better correlation with human judgement than the Data-QuestEval metric which was previously proposed for evaluating the semantic adequacy of KG-to-text generations.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2023 GeneratingandAnsweringSimpleand	Kelvin Han Claire Gardent			Generating and Answering Simple and Complex Questions from Text and from Knowledge Graphs						2023