TY - GEN
T1 - Automatic Knowledge Fusion in Transferrable Networks for Semantic Text Matching
AU - Chen, Cen
AU - Zhang, Ya Lin
AU - Qiu, Minghui
AU - Wu, Bingzhe
AU - Wang, Li
AU - Li, Longfei
AU - Zhou, Jun
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - Transfer learning has been widely studied in many real-world applications to help the model performance of data-deficient domains. However, the design choice, such as what, where and how to share, can greatly influence the model performance. To save the effort in the transfer learning model design, we propose a novel method to automatically fuse transferable network architecture. Extensive experiments on public datasets of semantic text matching tasks show that our proposed method has better performance than the state-of-the-art transfer learning architectures.
AB - Transfer learning has been widely studied in many real-world applications to help the model performance of data-deficient domains. However, the design choice, such as what, where and how to share, can greatly influence the model performance. To save the effort in the transfer learning model design, we propose a novel method to automatically fuse transferable network architecture. Extensive experiments on public datasets of semantic text matching tasks show that our proposed method has better performance than the state-of-the-art transfer learning architectures.
UR - https://www.scopus.com/pages/publications/85091694844
U2 - 10.1145/3366424.3382703
DO - 10.1145/3366424.3382703
M3 - 会议稿件
AN - SCOPUS:85091694844
T3 - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
SP - 73
EP - 74
BT - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery
T2 - 29th International World Wide Web Conference, WWW 2020
Y2 - 20 April 2020 through 24 April 2020
ER -