Zero-shot Question Generation From Knowledge Graphs For Unseen Predicates And Entity Types
Hady Elsahar, Christophe Gravier, Frederique Laforest . Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) 2018 – 78 citations
We present a neural model for question generation from knowledge base triples in a “Zero-Shot” setup, that is generating questions for triples containing predicates, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in an encoder-decoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model sets a new state-of-the-art for zero-shot QG.