We describe the systems developed for the WNUT-2020 shared task 2,
identification of informative COVID-19 English Tweets. BERT is a highly
performant model for Natural Language Processing tasks. We increased BERT's
performance in this classification task by fine-tuning BERT and concatenating
its embeddings with Tweet-specific features and training a Support Vector
Machine (SVM) for classification (henceforth called BERT+). We compared its
performance to a suite of machine learning models. We used a Twitter specific
data cleaning pipeline and word-level TF-IDF to extract features for the
non-BERT models. BERT+ was the top performing model with an F1-score of 0.8713.

该篇论文描述了在 WNUT-2020 共享任务 2 中开发的针对 COVID-19 英文推文信息识别的系统。研究者团队利用 BERT 模型进行了文本分类任务，并且通过对 BERT 模型的微调以及将其嵌入特征与推文特定特征连接后再采用支持向量机（SVM）进行训练（即 BERT +），并与一组机器学习模型进行性能比较。结果表明 BERT + 模型在 F1-score 评估指标上得分最高为 0.8713。

使用 BERT 对具有信息价值的 COVID-19 推文进行分类的 WNUT-2020 任务 2 的 Dartmouth CS

Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet  Classification Using BERT

In this paper, we provide an overview of the WNUT-2020 shared task on the
identification of informative COVID-19 English Tweets. We describe how we
construct a corpus of 10K Tweets and organize the development and evaluation
phases for this task. In addition, we also present a brief summary of results
obtained from the final system evaluation submissions of 55 teams, finding that
(i) many systems obtain very high performance, up to 0.91 F1 score, (ii) the
majority of the submissions achieve substantially higher results than the
baseline fastText (Joulin et al., 2017), and (iii) fine-tuning pre-trained
language models on relevant language data followed by supervised training
performs well in this task.

本文概述了 WNUT-2020 共享任务，重点是识别有信息含义的 COVID-19 英文推文。文章介绍了 10k 推文语料库的建立、开发和评估各阶段的组织、55 个团队提交的最终系统评估结果，并发现大多数提交结果明显高于 fastText 基线。本研究发现在相关语言数据调整预训练语言模型并进行监督训练可以在这个任务中得到很好的表现。