Paraphrase identification using collaborative adversarial networks.
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| Title: | Paraphrase identification using collaborative adversarial networks. |
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| Authors: | Alzubi, Jafar A.1 (AUTHOR) j.zubi@bau.edu.jo, Jain, Rachna2 (AUTHOR), Kathuria, Abhishek2 (AUTHOR), Khandelwal, Anjali2 (AUTHOR), Saxena, Anmol2 (AUTHOR), Singh, Anubhav2 (AUTHOR) |
| Source: | Journal of Intelligent & Fuzzy Systems. 2020, Vol. 39 Issue 1, p1021-1032. 12p. |
| Subjects: | Paraphrase, Information commons, Identification, Feature extraction |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10641246 |
| DOI: | 10.3233/JIFS-191933 |
