IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization

• URL: http://arxiv.org/abs/2109.04607v1
• Date: Fri, 10 Sep 2021 01:27:51 GMT
• Title: IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization
• Authors: Fajri Koto and Jey Han Lau and Timothy Baldwin
• Abstract summary: IndoBERTweet is the first large-scale pretrained model for Indonesian Twitter. We 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.
• Score: 33.46519116869276
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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