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- W4288404295 abstract "State-of-the-art methods for text classification include several distinct steps of pre-processing, feature extraction and post-processing. In this work, we focus on end-to-end neural architectures and show that the best performance in text classification is obtained by combining information from different neural modules. Concretely, we combine convolution, recurrent and attention modules with ensemble methods and show that they are complementary. We introduce ECGA, an end-to-end go-to architecture for novel text classification tasks. We prove that it is efficient and robust, as it attains or surpasses the state-of-the-art on varied datasets, including both low and high data regimes." @default.
- W4288404295 created "2022-07-29" @default.
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- W4288404295 date "2019-03-28" @default.
- W4288404295 modified "2023-09-28" @default.
- W4288404295 title "Resilient Combination of Complementary CNN and RNN Features for Text Classification through Attention and Ensembling" @default.
- W4288404295 doi "https://doi.org/10.48550/arxiv.1903.12157" @default.
- W4288404295 hasPublicationYear "2019" @default.
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