Resilient Combination of Complementary CNN and RNN Features for Text\n Classification through Attention and Ensembling

2019/03/28 by Athanasios Giannakopoulos, Maxime Coriou, Giannakopoulos, Athanasios +7
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Machine Learning and Data Classification #Text and Document Classification Technologies

paper · pdf · doi:10.48550/arxiv.1903.12157

openalex publication_date 2019/03/28 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28

Abstract

State-of-the-art methods for text classification include several distinct\nsteps of pre-processing, feature extraction and post-processing. In this work,\nwe focus on end-to-end neural architectures and show that the best performance\nin text classification is obtained by combining information from different\nneural modules. Concretely, we combine convolution, recurrent and attention\nmodules with ensemble methods and show that they are complementary. We\nintroduce ECGA, an end-to-end go-to architecture for novel text classification\ntasks. We prove that it is efficient and robust, as it attains or surpasses the\nstate-of-the-art on varied datasets, including both low and high data regimes.\n

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