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Natural language inference intends to predict whether a hypothesis sentence can be inferred from the premise sentence. Most prior works rely on a simplistic association between the premise and hypothesis sentence pairs, which is not sufficient for learning complex relationships between them. The strategy also fails to exploit local context information fully. Long Short Term Memory (LSTM) or gated recurrent units networks (GRU) are not effective in modeling long-term dependencies, and their schemes are far more complex as compared to Convolutional Neural Networks (CNN). To address this problem of long-term dependency, and to involve context for modeling better representation of a sentence, in this article, a general Self-Attentive Convolution Neural Network (SACNN) is presented for natural language inference and sentence pair modeling tasks. The proposed model uses CNNs to integrate mutual interactions between sentences, and each sentence with their counterparts is taken into consideration for the formulation of their representation. Moreover, the self-attention mechanism helps fully exploit the context semantics and long-term dependencies within a sentence. Experimental results proved that SACNN was able to outperform strong baselines and achieved an accuracy of 89.7% on the stanford natural language inference (SNLI) dataset.<\/jats:p>","DOI":"10.1145\/3426884","type":"journal-article","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T13:37:22Z","timestamp":1623850642000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["SACNN: Self-attentive Convolutional Neural Network Model for Natural Language Inference"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9087-0242","authenticated-orcid":false,"given":"Waris","family":"Quamer","sequence":"first","affiliation":[{"name":"Indian Institute of Technology (ISM) Dhanbad, Dhanbad, Jharkhand, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7651-4444","authenticated-orcid":false,"given":"Praphula Kumar","family":"Jain","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (ISM) Dhanbad, Dhanbad, Jharkhand, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arpit","family":"Rai","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (ISM) Dhanbad, Dhanbad, Jharkhand, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vijayalakshmi","family":"Saravanan","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Rochester Institute of Technology, Rochester, NJ, US"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajendra","family":"Pamula","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (ISM), Dhanbad, Jharkhand, Dhanbad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chiranjeev","family":"Kumar","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (ISM), Dhanbad, Jharkhand, Dhanbad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1751277"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/11736790_9"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3115\/1119239.1119245"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3110856"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-5301"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-4016"},{"volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. 632\u2013642","author":"Bowman Samuel","key":"e_1_2_1_7_1","unstructured":"Samuel Bowman , Gabor Angeli , Christopher Potts , and Christopher D. 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