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

Proceedings of NAACL-HLT 2018, pages 218–228 New Orleans, Louisiana, June 1 - 6, 2018. c⃝2018 Association for Computational Linguistics Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types Hady Elsahar, Christophe Gravier, Frederique Laforest Universit´e de Lyon Laboratoire Hubert Curien Saint-´Etienne, France {firstname.lastname}@univ-st-etienne.fr Abstract We present a neural model for question gener- ation 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 gen- erate questions. Benchmark and human evalu- ation show that our model sets a new state-of- the-art for zero-shot QG. 1 Introduction Questions Generation (QG) from Knowledge Graphs is the task consisting in generating natural language questions given an input knowledge base (KB) triple (Serban et al., 2016). QG from knowledge graphs has shown to improve the performance of existing factoid question answer- ing (QA) systems either by dual training or by augmenting existing training datasets (Dong et al., 2017; Khapra et al., 2017). Those methods rely on large-scale annotated datasets such as Simple- Questions (Bordes et al., 2015). Building such datasets is a tedious task in practice, especially to obtain an unbiased dataset – i.e. a dataset that covers equally a large amount of triples in the KB. In practice many of the predicates and entity types in KB are not covered by those annotated datasets. For example 75.6% of Freebase predicates are not covered by the SimpleQuestions dataset 1. Among those we can find important missing predicates such as: fb:food/beer/country, fb:location/country/national anthem, fb:astronomy/star system/stars. One challenge for QG from knowledge graphs is to adapt to predicates and entity types that 1replicate the observation http://bit.ly/2GvVHae were not seen at training time (Zero-Shot Ques- tion Generation). Since state-of-the-art systems in factoid QA rely on the tremendous efforts made to create SimpleQuestions, these systems can only process questions on the subset of 24.4% of free- base predicates defined in SimpleQuestions. Pre- vious works for factoid QG (Serban et al., 2016) claims to solve the issue of small size QA datasets. However encountering an unseen predicate / entity type will generate questions made out of random text generation for those out-of-vocabulary predi- cates a QG system had never seen. We go beyond this state-of-the-art by providing an original and non-trivial solution for creating a much broader set of questions for unseen predicates and entity types. Ultimately, generating questions to predi- cates and entity types unseen at training time will allow QA systems to cover predicates and entity types that would not have been used for QA other- wise. Intuitively, a human who is given the task to write a question on a fact offered by a KB, would read natural language sentences where the entity or the predicate of the fact occur, and build up questions that are aligned with what he reads …

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