Identifying informative tweets is an important step when building information
extraction systems based on social media. WNUT-2020 Task 2 was organised to
recognise informative tweets from noise tweets. In this paper, we present our
approach to tackle the task objective using transformers. Overall, our approach
achieves 10th place in the final rankings scoring 0.9004 F1 score for the test
set.

使用 transformers 模型的方法识别社交媒体上的信息性推文，取得了 WNUT-2020 第 10 名的 F1 分数 0.9004。

WNUT-2020 任务 2 中的 InfoMiner：基于 Transformer 的 Covid-19 信息推文提取

InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative  Tweet Extraction

Recently, COVID-19 has affected a variety of real-life aspects of the world
and led to dreadful consequences. More and more tweets about COVID-19 has been
shared publicly on Twitter. However, the plurality of those Tweets are
uninformative, which is challenging to build automatic systems to detect the
informative ones for useful AI applications. In this paper, we present our
results at the W-NUT 2020 Shared Task 2: Identification of Informative COVID-19
English Tweets. In particular, we propose our simple but effective approach
using the transformer-based models based on COVID-Twitter-BERT (CT-BERT) with
different fine-tuning techniques. As a result, we achieve the F1-Score of
90.94\% with the third place on the leaderboard of this task which attracted 56
submitted teams in total.

以 COVID-Twitter-BERT (CT-BERT) 为基础的变形器模型和微调技术可以有效地识别有用的 COVID-19 相关的 Twitter 帖子，获得了 2020 年 W-NUT 共享任务 2 中排名第三的 F1 分数为 90.94％