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

## TL;DR

This paper presents ECGA, an end-to-end neural architecture that combines CNN, RNN, and attention modules with ensembling to improve text classification performance across diverse datasets.

## Contribution

The paper introduces ECGA, a novel architecture that effectively integrates multiple neural modules and ensembling for robust, high-performing text classification.

## Key findings

- ECGA surpasses state-of-the-art on various datasets.
- It is effective in both low and high data regimes.
- The combination of modules is shown to be complementary.

## 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.