Abstract
Question generation from knowledge bases (or knowledge base question generation, KBQG) is the task of generating questions from structured database information, typically in the form of triples representing facts. To handle rare entities and generalize to unseen properties, previous work on KBQG resorted to extensive, often ad-hoc pre- and post-processing of the input triple. We revisit KBQG – using pre training, a new (triple, question) dataset and taking question type into account – and show that our approach outperforms previous work both in a standard and in a zero-shot setting. We also show that the extended KBQG dataset (also helpful for knowledge base question answering) we provide allows not only for better coverage in terms of knowledge base (KB) properties but also for increased output variability in that it permits the generation of multiple questions from the same KB triple.- Anthology ID:
- 2022.lrec-1.29
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 277–290
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.29
- DOI:
- Cite (ACL):
- Kelvin Han, Thiago Castro Ferreira, and Claire Gardent. 2022. Generating Questions from Wikidata Triples. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 277–290, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Generating Questions from Wikidata Triples (Han et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lrec-1.29.pdf
- Data
- DBpedia, SimpleQuestions