Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network

• Published in: PeerJ. Computer science Vol. 8; p. e908
• Main Authors: Zheng, Wenfeng, Yin, Lirong
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 12.04.2022; PeerJ, Inc; PeerJ Inc
• Subjects: Artificial Intelligence; Characterization inference; Cognition & reasoning; Computational linguistics; Datasets; Deep fusion matching network; Deep learning; Entrance examinations; Joint-Optimization of Multi-layer semantics; Language; Language processing; Machine translation; Matching; Meta-learning; Methods; Modules; Multilayers; Natural Language and Speech; Natural language interfaces; Natural language processing; Natural language reasoning; Neural networks; Optimization; Reasoning; Representations; Semantics; Characterization inference; Natural language reasoning; Joint-Optimization of Multi-layer semantics; Deep fusion matching network; Meta-learning

The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. This paper combines the multi-layer semantic representation network with the deep fusion matching network to solve the limitations of only considering a sent...