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arXiv 2008.13160cs.CLcs.LGcs.SI

QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions

Queen Mary University of London(伦敦玛丽女王大学);Imam Abdulrahman bin Faisal University(伊玛目阿卜杜勒拉赫曼·本·费萨尔大学);University of Oxford(牛津大学);University of Warwick(华威大学);Alan Turing Institute(艾伦·图灵研究所)

Rabab Alkhalifa, Theodore Yoong, Elena Kochkina, Arkaitz Zubiaga, Maria Liakata

英文摘要:

This paper describes the participation of the QMUL-SDS team for Task 1 of the CLEF 2020 CheckThat! shared task. The purpose of this task is to determine the check-worthiness of tweets about COVID-19 to identify and prioritise tweets that need fact-checking. The overarching aim is to further support ongoing efforts to protect the public from fake news and help people find reliable information. We describe and analyse the results of our submissions. We show that a CNN using COVID-Twitter-BERT (CT-BERT) enhanced with numeric expressions can effectively boost performance from baseline results. We also show results of training data augmentation with rumours on other topics. Our best system ranked fourth in the task with encouraging outcomes showing potential for improved results in the future.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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