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has been cited by the following article:

• TITLE: Towards Understanding Creative Language in Tweets

  AUTHORS: Linrui Zhang, Yisheng Zhou, Yang Yu, Dan Moldovan

  KEYWORDS: Natural Language Processing, Deep Learning, Transfer Learning

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.12 No.11, November 18, 2019

  ABSTRACT: Extracting fine-grained information from social media is traditionally a challenging task, since the language used in social media messages is usually informal, with creative genre-specific terminology and expression. How to handle such a challenge so as to automatically understand the opinions that people are communicating has become a hot subject of research. In this paper, we aim to show that leveraging the pre-learned knowledge can help neural network models understand the creative language in Tweets. In order to address this idea, we present a transfer learning model based on BERT. We fine-turned the pre-trained BERT model and applied the customized model to two downstream tasks described in SemEval-2018: Irony Detection task and Emoji Prediction task of Tweets. Our model could achieve an F-score of 38.52 (ranked 1/49) in Emoji Prediction task and 67.52 (ranked 2/43) and 51.35 (ranked 1/31) in Irony Detection subtask A and subtask B. The experimental results validate the effectiveness of our idea.