التفاصيل البيبلوغرافية
| العنوان: |
S2‐Net: Machine reading comprehension with SRU‐based self‐matching networks. |
| المؤلفون: |
Park, Cheoneum, Lee, Changki, Hong, Lynn, Hwang, Yigyu, Yoo, Taejoon, Jang, Jaeyong, Hong, Yunki, Bae, Kyung‐Hoon, Kim, Hyun‐Ki |
| المصدر: |
ETRI Journal; Jun2019, Vol. 41 Issue 3, p371-382, 12p |
| مصطلحات موضوعية: |
READING comprehension, RECURRENT neural networks, COMPREHENSION testing, MACHINING, SHORT-term memory, MOSQUITO nets |
| مستخلص: |
Machine reading comprehension is the task of understanding a given context and finding the correct response in that context. A simple recurrent unit (SRU) is a model that solves the vanishing gradient problem in a recurrent neural network (RNN) using a neural gate, such as a gated recurrent unit (GRU) and long short‐term memory (LSTM); moreover, it removes the previous hidden state from the input gate to improve the speed compared to GRU and LSTM. A self‐matching network, used in R‐Net, can have a similar effect to coreference resolution because the self‐matching network can obtain context information of a similar meaning by calculating the attention weight for its own RNN sequence. In this paper, we construct a dataset for Korean machine reading comprehension and propose an S2‐Net model that adds a self‐matching layer to an encoder RNN using multilayer SRU. The experimental results show that the proposed S2‐Net model has performance of single 68.82% EM and 81.25% F1, and ensemble 70.81% EM, 82.48% F1 in the Korean machine reading comprehension test dataset, and has single 71.30% EM and 80.37% F1 and ensemble 73.29% EM and 81.54% F1 performance in the SQuAD dev dataset. [ABSTRACT FROM AUTHOR] |
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