ETRI Journal

  • 제41권6호
  • /
  • Pages.771-781
  • /
  • 2019
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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VS3-NET: Neural variational inference model for machine-reading comprehension

  • Park, Cheoneum (Computer Science Departmemt, Kangwon National University) ;
  • Lee, Changki (Computer Science Departmemt, Kangwon National University) ;
  • Song, Heejun (Artificial Intelligence Center, Samsung Electronics Co., Samsung Research)
  • 투고 : 2018.08.20
  • 심사 : 2019.02.25
  • 발행 : 2019.12.06
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초록

We propose the VS3-NET model to solve the task of question answering questions with machine-reading comprehension that searches for an appropriate answer in a given context. VS3-NET is a model that trains latent variables for each question using variational inferences based on a model of a simple recurrent unit-based sentences and self-matching networks. The types of questions vary, and the answers depend on the type of question. To perform efficient inference and learning, we introduce neural question-type models to approximate the prior and posterior distributions of the latent variables, and we use these approximated distributions to optimize a reparameterized variational lower bound. The context given in machine-reading comprehension usually comprises several sentences, leading to performance degradation caused by context length. Therefore, we model a hierarchical structure using sentence encoding, in which as the context becomes longer, the performance degrades. Experimental results show that the proposed VS3-NET model has an exact-match score of 76.8% and an F1 score of 84.5% on the SQuAD test set.

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피인용 문헌

  1. Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network vol.2021, 2019, https://doi.org/10.1155/2021/5100809