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Multimodal News Feed Evaluation System with Deep Reinforcement Learning Approaches

Published:09 March 2021Publication History
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Abstract

Multilingual and multimodal data analysis is the emerging news feed evaluation system. News feed analysis and evaluations are interrelated processes, which are useful in understanding the news factors. The news feed evaluation system can be implemented for single or multilingual language models. Classification techniques used on multilingual news analysis require deep layered learning techniques rather than conventional approaches. In this proposed work, a hierarchical structure of deep learning algorithms is implemented for making an effective complex news evaluation system. Deep learning techniques such as the Deep Cooperative Multilingual Reinforcement Learning Model, the Multidimensional Genetic Algorithm, and the Multilingual Generative Adversarial Network are developed to evaluate a vast number of news feeds. The proposed tech-niques collaborate in a pipeline order to build a deep news feed evaluation system. The implementation details project that the newly proposed system performs 5% to 12% better than the other news evaluation systems.

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        • Published in

          cover image ACM Transactions on Asian and Low-Resource Language Information Processing
          ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 1
          Special issue on Deep Learning for Low-Resource Natural Language Processing, Part 1 and Regular Papers
          January 2021
          332 pages
          ISSN:2375-4699
          EISSN:2375-4702
          DOI:10.1145/3439335
          Issue’s Table of Contents

          Copyright © 2021 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 9 March 2021
          • Accepted: 1 July 2020
          • Revised: 1 June 2020
          • Received: 1 February 2020
          Published in tallip Volume 20, Issue 1

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