%0 Generic %1 muller2020covidtwitterbert %A Müller, Martin %A Salathé, Marcel %A Kummervold, Per E %D 2020 %K antrag bert covid deconspire language model nlp twitter %T COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter %U http://arxiv.org/abs/2005.07503 %X In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.

@misc{muller2020covidtwitterbert, abstract = {In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.}, added-at = {2020-09-14T23:04:08.000+0200}, author = {Müller, Martin and Salathé, Marcel and Kummervold, Per E}, biburl = {https://www.bibsonomy.org/bibtex/28cd18e9d5662cb1036a86e066a6e0df4/schwemmlein}, description = {COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter}, interhash = {7e5919fdd413eae4c980e7897c98c356}, intrahash = {8cd18e9d5662cb1036a86e066a6e0df4}, keywords = {antrag bert covid deconspire language model nlp twitter}, note = {cite arxiv:2005.07503}, timestamp = {2020-09-14T23:04:08.000+0200}, title = {COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter}, url = {http://arxiv.org/abs/2005.07503}, year = 2020 }