Cheoneum Park  Changki Lee  Sulyn Hong  Yigyu Hwang  Taejoon Yoo  Hyunki Kim

10.5626/JOK.2018.45.12.1260

Machine Reading Comprehension question answering simple recurrent unit self-matching network Korean machine reading comprehension dataset

Machine reading comprehension is the task of understanding a given context and identifying the right answer in context. Simple recurrent unit (SRU) solves the vanishing gradient problem in recurrent neural network (RNN) by using neural gate such as gated recurrent unit (GRU), and removes previous hidden state from gate input to improve speed. Self-matching network is used in r-net, and this has a similar effect as coreference resolution can show similar semantic context information by calculating attention weight for its RNN sequence. In this paper, we propose a S²-Net model that add self-matching layer to an encoder using stacked SRUs and constructs a Korean machine reading comprehension dataset. Experimental results reveal the proposed S²-Net model has EM 70.81% and F1 82.48% performance in Korean machine reading comprehension.