Evaluating Transformer-Based Multilingual Text Classification
Abstract
As NLP tools become ubiquitous in today's technological landscape, they are increasingly applied to languages with a variety of typological structures. However, NLP research does not focus primarily on typological differences in its analysis of state-of-the-art language models. As a result, NLP tools perform unequally across languages with different syntactic and morphological structures. Through a detailed discussion of word order typology, morphological typology, and comparative linguistics, we identify which variables most affect language modeling efficacy; in addition, we calculate word order and morphological similarity indices to aid our empirical study. We then use this background to support our analysis of an experiment we conduct using multi-class text classification on eight languages and eight models.
Cite
@article{arxiv.2004.13939,
title = {Evaluating Transformer-Based Multilingual Text Classification},
author = {Sophie Groenwold and Samhita Honnavalli and Lily Ou and Aesha Parekh and Sharon Levy and Diba Mirza and William Yang Wang},
journal= {arXiv preprint arXiv:2004.13939},
year = {2020}
}
Comments
Total of 15 pages (9 pages for paper, 2 pages for references, 4 pages for appendix). Changed title