2026-09-18T20:17:19Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/107875 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Chang, Kevin C.-C. Wei, Bingzhe 2020-08-26T21:54:17Z 2020-08-26T21:54:17Z 2020-04-13 2020-05 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. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms The student, Bingzhe Wei, accepted the attached license on 2020-04-09 at 19:17. The student, Bingzhe Wei, submitted this Thesis for approval on 2020-04-10 at 17:23. This Thesis was approved for publication on 2020-04-13 at 16:46. DSpace SAF Submission Ingestion Package generated from Vireo submission #14951 on 2020-08-25 at 17:06:42 Made available in DSpace on 2020-08-26T21:54:17Z (GMT). No. of bitstreams: 2 WEI-THESIS-2020.pdf: 18679108 bytes, checksum: 3e565c6cec35d43d1368319ffb7be86b (MD5) LICENSE.txt: 4208 bytes, checksum: 7f75bb514e88429786c655a25293722d (MD5) Previous issue date: 2020-04-13 application/pdf http://hdl.handle.net/2142/107875 en Copyright 2020 Bingzhe Wei question generation variational autoencoders machine learning natural language processing Synonymous question generation: Learning to ask in different ways using variational autoencoders text Thesis Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Thesis M.S.