EMNLP 2021main88 citations
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization
Fajri Koto, Jey Han Lau, Timothy Baldwin
Abstract
We present IndoBERTweet, the first large-scale pretrained model for Indonesian Twitter that is trained by extending a monolingually-trained Indonesian BERT model with additive domain-specific vocabulary. We focus in particular on efficient model adaptation under vocabulary mismatch, and benchmark different ways of initializing the BERT embedding layer for new word types. We find that initializing with the average BERT subword embedding makes pretraining five times faster, and is more effective than proposed methods for vocabulary adaptation in terms of extrinsic evaluation over seven Twitter-based datasets.
BibTeX
@inproceedings{koto-etal-2021-indobertweet,
title = "{I}ndo{BERT}weet: A Pretrained Language Model for {I}ndonesian {T}witter with Effective Domain-Specific Vocabulary Initialization",
author = "Koto, Fajri and
Lau, Jey Han and
Baldwin, Timothy",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.833/",
doi = "10.18653/v1/2021.emnlp-main.833",
pages = "10660--10668"
}