We present ALL-IN-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.
@article{arxiv.1710.09589,
title = {ALL-IN-1: Short Text Classification with One Model for All Languages},
author = {Barbara Plank},
journal= {arXiv preprint arXiv:1710.09589},
year = {2017}
}