Synonymous question generation: Learning to ask in different ways using variational autoencoders
Wei, Bingzhe
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https://hdl.handle.net/2142/107875
Description
Title
Synonymous question generation: Learning to ask in different ways using variational autoencoders
Author(s)
Wei, Bingzhe
Issue Date
2020-04-13
Director of Research (if dissertation) or Advisor (if thesis)
Chang, Kevin C.-C.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
question generation
variational autoencoders
machine learning
natural language processing
Abstract
Recently, there has been significant interest in advancing machine comprehension of text through question answering. Motivated by the idea that machine comprehension should be bidirectional, we explore synonymous question generation from knowledge graphs (KGs) to enable machines to learn how to ask semantically equivalent natural language questions with lexical and syntactical variety from KGs. To the best of our knowledge, this problem has not yet been explored in the literature. We propose explicitly modeling variations in natural language questions associated with KG triples through a conditional variational autoencoder-based model, the Template VAE (T-VAE). Evaluating the generated questions via the Fre'chet InferSent Distance (FID) and the Multiset-Jaccard-k-gram (MS-Jaccard-k) Measure, two joint diversity-quality metrics, demonstrates that the proposed model is able to produce fluent questions that accurately capture variations in questions associated with KG triples. Depending on test conditions, the T-VAE achieves a 15-21% improvement in MS-Jaccard-4 score and a 29-47% improvement in FID score relative to baseline methods.
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