Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data

Colin Leong, Daniel Whitenack


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.
Anthology ID:
2022.acl-long.364
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5306–5315
Language:
URL:
https://aclanthology.org/2022.acl-long.364
DOI:
10.18653/v1/2022.acl-long.364
Bibkey:
Cite (ACL):
Colin Leong and Daniel Whitenack. 2022. Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5306–5315, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data (Leong & Whitenack, ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.364.pdf
Video:
 https://aclanthology.org/2022.acl-long.364.mp4
Code
 sil-ai/phone-it-in
Data
MasakhaNER