This paper presents our approaches for the SMM4H24 Shared Task 5 on the
binary classification of English tweets reporting children's medical disorders.
Our first approach involves fine-tuning a single RoBERTa-large model, while the
second approach entails ensembling the results of three fine-tuned
BERTweet-large models. We demonstrate that although both approaches exhibit
identical performance on validation data, the BERTweet-large ensemble excels on
test data. Our best-performing system achieves an F1-score of 0.938 on test
data, outperforming the benchmark classifier by 1.18%.

通过对英文推文中儿童医学疾病进行二分类，本文介绍了我们在 SMM4H24 共享任务 5 中的方法。第一种方法是对一个 RoBERTa-large 模型进行微调，而第二种方法则是对三个微调的 BERTweet-large 模型结果进行集成。我们证明尽管两种方法在验证数据上表现一致，但 BERTweet-large 集成模型在测试数据上表现出色。我们最佳的系统在测试数据上的 F1 分数达到了 0.938，超过了基准分类器 1.18%。

LT4SG@SMM4H24：利用预训练语言模型对儿童健康结果进行数字流行病学推断的推文分类

LT4SG@SMM4H24: Tweets Classification for Digital Epidemiology of  Childhood Health Outcomes Using Pre-Trained Language Models

As hate speech continues to proliferate on the web, it is becoming
increasingly important to develop computational methods to mitigate it.
Reactively, using black-box models to identify hateful content can perplex
users as to why their posts were automatically flagged as hateful. On the other
hand, proactive mitigation can be achieved by suggesting rephrasing before a
post is made public. However, both mitigation techniques require information
about which part of a post contains the hateful aspect, i.e., what spans within
a text are responsible for conveying hate. Better detection of such spans can
significantly reduce explicitly hateful content on the web. To further
contribute to this research area, we organized HateNorm at HASOC-FIRE 2023,
focusing on explicit span detection in English Tweets. A total of 12 teams
participated in the competition, with the highest macro-F1 observed at 0.58.

网上仇恨言论的扩散导致了对开发计算方法进行缓解的重要性的不断增加。为了更好地检测具有仇恨性质的文本片段，引入了反应式和主动缓解技术。本研究主要关注在英文推文中检测明确的文本片段，并举办了 HateNorm 项目来进一步促进这一研究领域。

2023 年 FIRE HASOC 子任务概述：通过跨度检测识别英文明确仇恨相关令牌

Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens  Contributing to Explicit Hate in English by Span Detection

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。