# Fully Convolutional Networks for Text Classification

## TL;DR

This paper introduces a novel approach using fully convolutional networks for text classification, exploring attention modifications, and analyzing their effects on performance with discussions on limitations and future improvements.

## Contribution

It presents a new application of fully convolutional networks for variable-sized text classification and evaluates attention modifications with insights into their impact.

## Key findings

- Suboptimal results on ITAmoji 2018 dataset
- Analysis of attention modification effects
- Discussion on potential improvements

## Abstract

In this work I propose a new way of using fully convolutional networks for classification while allowing for input of any size. I additionally propose two modifications on the idea of attention and the benefits and detriments of using the modifications. Finally, I show suboptimal results on the ITAmoji 2018 tweet to emoji task and provide a discussion about why that might be the case as well as a proposed fix to further improve results.

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Source: https://tomesphere.com/paper/1902.05575