TY  - JOUR
T1  - Korean Machine Reading Comprehension with S²-Net
AU  - Park, Cheoneum 
AU  - Lee, Changki 
AU  - Hong, Sulyn 
AU  - Hwang, Yigyu 
AU  - Yoo, Taejoon 
AU  - Kim, Hyunki 
JO  - Journal of KIISE, JOK
PY  - 2018
DA  - 2018/1/14
DO  - 10.5626/JOK.2018.45.12.1260
KW  - machine reading comprehension
KW  - question answering
KW  - simple recurrent unit
KW  - self-matching network
KW  - Korean machine reading comprehension dataset
AB  - 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.