TY - GEN
T1 - Capsule Networks for Chinese Opinion Questions Machine Reading Comprehension
AU - Ding, Longxiang
AU - Li, Zhoujun
AU - Wang, Boyang
AU - He, Yueying
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - In recent years, machine reading comprehension is becoming a more and more popular research topic. Promising results were obtained when the machine reading comprehension task had only two inputs, context and query. In this paper, we propose a capsule networks based model for Chinese opinion machine reading comprehension task which has three inputs: context, query and alternatives. First, we use a bi-directional LSTM to encode the three inputs. Second, model the complex interactions between context and query with a multiway attention layer. In addition to the attention mechanism used in BiDAF, the other two attention functions are designed to match the relationship between inputs. Finally, we present a capsule networks layer to route the right alternative. Specifically, we use two strategies to improve the dynamic routing process to filter noisy capsules, which may contain useless information such as stop words. Our single model achieves competitive results compared to the baseline methods on a Chinese dataset and obtains a significant improvement of 2.45% accuracy.
AB - In recent years, machine reading comprehension is becoming a more and more popular research topic. Promising results were obtained when the machine reading comprehension task had only two inputs, context and query. In this paper, we propose a capsule networks based model for Chinese opinion machine reading comprehension task which has three inputs: context, query and alternatives. First, we use a bi-directional LSTM to encode the three inputs. Second, model the complex interactions between context and query with a multiway attention layer. In addition to the attention mechanism used in BiDAF, the other two attention functions are designed to match the relationship between inputs. Finally, we present a capsule networks layer to route the right alternative. Specifically, we use two strategies to improve the dynamic routing process to filter noisy capsules, which may contain useless information such as stop words. Our single model achieves competitive results compared to the baseline methods on a Chinese dataset and obtains a significant improvement of 2.45% accuracy.
KW - Capsule networks
KW - Machine reading comprehension
KW - Multiway attention
UR - https://www.scopus.com/pages/publications/85075753240
U2 - 10.1007/978-3-030-32381-3_42
DO - 10.1007/978-3-030-32381-3_42
M3 - 会议稿件
AN - SCOPUS:85075753240
SN - 9783030323806
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 521
EP - 532
BT - Chinese Computational Linguistics - 18th China National Conference, CCL 2019, Proceedings
A2 - Sun, Maosong
A2 - Liu, Yang
A2 - Liu, Zhiyuan
A2 - Huang, Xuanjing
A2 - Ji, Heng
PB - Springer
T2 - 18th China National Conference on Computational Linguistics, CCL 2019
Y2 - 18 October 2019 through 20 October 2019
ER -