Korean Machine Reading Comprehension using S³-Net based on Position Encoding

Choeneum Park, Changki Lee, Hyunki Kim

http://doi.org/10.5626/JOK.2019.46.3.234

S³-Net is a deep learning model that is used in machine reading comprehension question answering (MRQA) based on Simple Recurrent Unit and Self-Matching Networks that calculates attention weight for own RNN sequence. The answers to the questions in the MRQA occur within the passage, because any passage is made up of several sentences, so the length of the input sequence becomes longer and the performance deteriorates. In this paper, a hierarchical model that adds sentence-level encoding and S³-Net that applies position encoding to check word order information to solve the problem of long-term context degradation are proposed. The experimental results show that the S³-Net model proposed in this paper has a performance of 69.43% in EM and 81.53% in F1 for single test, and 71.28% in EM and 82.67 in F1 for ensemble test.