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Recent studies on domain-specific BERT models show that effectiveness ondownstream tasks can be improved when models are pretrained on in-domain data.Often, the pretraining data used in these models are selected based on theirsubject matter, e.g., biology or computer science. Given the range ofapplications using social media text, and its unique language variety, wepretrain two models on tweets and forum text respectively, and empiricallydemonstrate the effectiveness of these two resources. In addition, weinvestigate how similarity measures can be used to nominate in-domainpretraining data. We publicly release our pretrained models athttps://bit.ly/35RpTf0.

Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media