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The content on the web is in a constant state of flux. New entities, issues,and ideas continuously emerge, while the semantics of the existing conversationtopics gradually shift. In recent years, pre-trained language models like BERTgreatly improved the state-of-the-art for a large spectrum of contentunderstanding tasks. Therefore, in this paper, we aim to study how theselanguage models can be adapted to better handle continuously evolving webcontent. In our study, we first analyze the evolution of 2013 - 2019 Twitterdata, and unequivocally confirm that a BERT model trained on past tweets wouldheavily deteriorate when directly applied to data from later years. Then, weinvestigate two possible sources of the deterioration: the semantic shift ofexisting tokens and the sub-optimal or failed understanding of new tokens. Tothis end, we both explore two different vocabulary composition methods, as wellas propose three sampling methods which help in efficient incremental trainingfor BERT-like models. Compared to a new model trained from scratch offline, ourincremental training (a) reduces the training costs, (b) achieves betterperformance on evolving content, and (c) is suitable for online deployment. Thesuperiority of our methods is validated using two downstream tasks. Wedemonstrate significant improvements when incrementally evolving the model froma particular base year, on the task of Country Hashtag Prediction, as well ason the OffensEval 2019 task.

Dynamic Language Models for Continuously Evolving Content