We experiment with COVID-Twitter-BERT and RoBERTa models to identify
informative COVID-19 tweets. We further experiment with adversarial training to
make our models robust. The ensemble of COVID-Twitter-BERT and RoBERTa obtains
a F1-score of 0.9096 (on the positive class) on the test data of WNUT-2020 Task
2 and ranks 1st on the leaderboard. The ensemble of the models trained using
adversarial training also produces similar result.

使用 COVID-Twitter-BERT 和 RoBERTa 模型实验性地识别 COVID-19 推文，进一步实验了对抗性训练，使我们的模型更加稳健，模型集成达到了 WNUT-2020 任务 2 测试数据中的 0.9096 的 F1 得分，并在排行榜上排名第一，对抗性训练的模型集成也产生了类似的结果。

WNUT-2020 任务 2: 使用集成和对抗训练稳健地识别有价值的 COVID-19 推文的 NutCracker

NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative  COVID-19 Tweets using Ensembling and Adversarial Training

Background Based Conversations (BBCs) have been developed to make dialogue
systems generate more informative and natural responses by leveraging
background knowledge. Existing methods for BBCs can be grouped into two
categories: extraction-based methods and generation-based methods. The former
extract spans frombackground material as responses that are not necessarily
natural. The latter generate responses thatare natural but not necessarily
effective in leveraging background knowledge. In this paper, we focus on
generation-based methods and propose a model, namely Context-aware Knowledge
Pre-selection (CaKe), which introduces a pre-selection process that uses
dynamic bi-directional attention to improve knowledge selection by using the
utterance history context as prior information to select the most relevant
background material. Experimental results show that our model is superior to
current state-of-the-art baselines, indicating that it benefits from the
pre-selection process, thus improving in-formativeness and fluency.

本研究提出了一种基于上下文感知的知识预选择模型，使用动态双向注意力机制来提高知识的选择，进而生成更具信息量和流畅的自然回复。实验结果表明，该模型在准确度和流畅度上都优于当前最先进的方法。