Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types

Hady Elsahar, Christophe Gravier, Frederique Laforest


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
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
218–228
Language:
URL:
https://www.aclweb.org/anthology/N18-1020
DOI:
10.18653/v1/N18-1020
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1020.pdf