Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data
Abstract
Multi-modal techniques offer significant untapped potential to unlock improved NLP technology for local languages. However, many advances in language model pre-training are focused on text, a fact that only increases systematic inequalities in the performance of NLP tasks across the world’s languages. In this work, we propose a multi-modal approach to train language models using whatever text and/or audio data might be available in a language. Initial experiments using Swahili and Kinyarwanda data suggest the viability of the approach for downstream Named Entity Recognition (NER) tasks, with models pre-trained on phone data showing an improvement of up to 6% F1-score above models that are trained from scratch. Preprocessing and training code will be uploaded to https://github.com/sil-ai/phone-it-in.
BibTeX
@inproceedings{leong-whitenack-2022-phone,
title = "Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data",
author = "Leong, Colin and
Whitenack, Daniel",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.364/",
doi = "10.18653/v1/2022.acl-long.364",
pages = "5306--5315"
}