  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.
