Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model

By Mitesh M. Khapra, Dinesh Raghu, Sachindra Joshi, Sathish Reddy. 2017

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In recent years, knowledge graphs such as Freebase that capture facts about entities and relationships between them have been used actively for answering factoid questions. In this paper, we explore the problem of automatically generating question answer pairs from a given knowledge graph. The generated question answer (QA) pairs can be used in several downstream applications. For example, they could be used for training better QA systems. To generate such QA pairs, we first extract a set of keywords from entities and relationships expressed in a triple stored in the knowledge graph. From each such set, we use a subset of keywords to generate a natural language question that has a unique answer. We treat this subset of keywords as a sequence and propose a sequence to sequence model using RNN to generate a natural language question from it. Our RNN based model generates QA pairs with an accuracy of 33.61 percent and performs 110.47 percent (relative) better than a state-of-the-art template based method for generating natural language question from keywords. We also do an extrinsic evaluation by using the generated QA pairs to train a QA system and observe that the F1-score of the QA system improves by 5.5 percent (relative) when using automatically generated QA pairs in addition to manually generated QA pairs available for training.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (2019, background): “Recently, we have seen an increasing interest in the QG area, with mainly three categories: Text-based QG (Du et al., 2017; Zhao et al., 2018), Knowledge-Base-based QG (Reddy et al., 2017; Serban et al., 2016), and Image-based QG (Li et al., 2018; Jain et al., 2017).”
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia (2018, methodology): “Ser-ban et al. (2016) used the encoder-decoder framework to generate QA pairs from knowledge base triples; Reddy et al. (2017) generated questions from a knowledge graph; Du et al. (2017) studied how to generate questions from sentences using an attention-based sequence-to-sequence model and…”
Multiple Choice Question Generation Utilizing An Ontology (2017, background): “Recent advances in text generation have used neural generative models to create interestingly worded questions (Serban et al., 2016; Indurthi et al., 2017).”
What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge (2019, background): “, 2019b), both of which involve standard crowd annotation to elicit question-answer pairs (see also Seyler et al., 2017; Reddy et al., 2017).”