Semantic Textual Similarity with Siamese Neural Networks

Tharindu Ranasinghe, Research Group in Computational Linguistics, University of Wolverhampton, UK, Constantin Orăsan, Ruslan Mitkov
2019 Proceedings - Natural Language Processing in a Deep Learning World  
Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods.
doi:10.26615/978-954-452-056-4_116 dblp:conf/ranlp/RanasingheOM19a fatcat:v3cgpzhvinhl3mn3ut5t3hcmne