Controversy is widespread online. Previous studies mainly define controversy
based on vague assumptions of its relation to sentiment such as hate speech and
offensive words. This paper introduces the first question-answering dataset
that defines content controversy by user perception, i.e., votes from plenty of
users. It contains nearly 10K questions, and each question has a best answer
and a most controversial answer. Experimental results reveal that controversy
detection in question answering is essential and challenging, and there is no
strong correlation between controversy and sentiment tasks.

本文介绍了第一个根据用户感知来定义争议性内容的问答数据集，并发现争议检测在问答过程中是必要且具有挑战性的，而争议与情感任务之间没有强关联。

ControversialQA：探究问题回答中的争议

ControversialQA: Exploring Controversy in Question Answering

Sentiment tasks such as hate speech detection and sentiment analysis,
especially when performed on languages other than English, are often
low-resource. In this study, we exploit the emotional information encoded in
emojis to enhance the performance on a variety of sentiment tasks. This is done
using a transfer learning approach, where the parameters learned by an
emoji-based source task are transferred to a sentiment target task. We analyse
the efficacy of the transfer under three conditions, i.e. i) the emoji content
and ii) label distribution of the target task as well as iii) the difference
between monolingually and multilingually learned source tasks. We find i.a.
that the transfer is most beneficial if the target task is balanced with high
emoji content. Monolingually learned source tasks have the benefit of taking
into account the culturally specific use of emojis and gain up to F1 +0.280
over the baseline.

该研究使用迁移学习的方法，利用表情符号编码的情感信息来提高各种情感任务的表现，发现单语言学习的源任务具有优势，并在三种条件下分析了转移学习的有效性。