Affiliations: [a] Faculty of Engineering, AL-Balqa Applied University, Salt – Jordan | [b] Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Correspondence:
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Corresponding author. Jafar A. Alzubi, Ph.D, Associate Professor, School of Engineering, Al-Balqa Applied University, School of Engineering, 19117, Jordan. Tel.: +962 001 336 582 3417; E-mail: j.zubi@bau.edu.jo.
Abstract: The paper presents a Collaborative Adversarial Network (CAN) model for paraphrase identification, which is a collaborative network holding generator that is pitted against an adversarial network called discriminator. There has been tremendous research work and countless examinations done on sentence similarity demonstration. Learning and identifying the constant highlights, specifically in various areas and domains is the main focus of paraphrase identification. It Involves the capture of regular highlights between two sentences and the community-oriented learning upon traditional ill-disposed and adversarial learning for common feature extraction. The model outperforms the MaLSTM model, which is the baseline model, and also proves to be comparable to many of the state-of-the-art techniques.
Keywords: Paraphrase identification, text classification, adversarial networks, LSTM, NLP
