Abstract
We present a neural model for question generation from knowledge graphs triples in a “Zero-shot” setup, that is generating questions for predicate, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in a encoder-decoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model outperforms state-of-the-art on this task.- Anthology ID:
- N18-1020
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 218–228
- Language:
- URL:
- https://aclanthology.org/N18-1020
- DOI:
- 10.18653/v1/N18-1020
- Cite (ACL):
- Hady Elsahar, Christophe Gravier, and Frederique Laforest. 2018. Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 218–228, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types (Elsahar et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N18-1020.pdf
- Code
- NAACL2018Anonymous/submission
- Data
- BEIR, SimpleQuestions