Intentionally luring readers to click on a particular content by exploiting
their curiosity defines a title as clickbait. Although several studies focused
on detecting clickbait titles in English articles, low resource language like
Bangla has not been given adequate attention. To tackle clickbait titles in
Bangla, we have constructed the first Bangla clickbait detection dataset
containing 15,056 labeled news articles and 65,406 unlabelled news articles
extracted from clickbait dense news sites. Each article has been labeled by
three expert linguists and includes an article's title, body, and other
metadata. By incorporating labeled and unlabelled data, we finetune a
pretrained Bangla transformer model in an adversarial fashion using Semi
Supervised Generative Adversarial Networks (SS GANs). The proposed model acts
as a good baseline for this dataset, outperforming traditional neural network
models (LSTM, GRU, CNN) and linguistic feature based models. We expect that
this dataset and the detailed analysis and comparison of these clickbait
detection models will provide a fundamental basis for future research into
detecting clickbait titles in Bengali articles. We have released the
corresponding code and dataset.

通过构建达到 15,056 个标记的新闻文章和 65,406 个未标记的新闻文章的第一个孟加拉 clickbait 检测数据集，应用 SS GANs 对 Semi Supervised Generative Adversarial Networks 进行微调，我们提出的新模型作为该数据集的一个良好基准，优于传统神经网络模型（LSTM、GRU、CNN）和基于语言特征的模型，这一研究将为未来检测孟加拉文 clickbait 标题提供一个基础。

BanglaBait: 孟加拉语点击诱饵数据集上的半监督对抗性方法

BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection  on Bangla Clickbait Dataset

We introduce the first very large detection dataset for event cameras. The
dataset is composed of more than 39 hours of automotive recordings acquired
with a 304x240 ATIS sensor. It contains open roads and very diverse driving
scenarios, ranging from urban, highway, suburbs and countryside scenes, as well
as different weather and illumination conditions. Manual bounding box
annotations of cars and pedestrians contained in the recordings are also
provided at a frequency between 1 and 4Hz, yielding more than 255,000 labels in
total. We believe that the availability of a labeled dataset of this size will
contribute to major advances in event-based vision tasks such as object
detection and classification. We also expect benefits in other tasks such as
optical flow, structure from motion and tracking, where for example, the large
amount of data can be leveraged by self-supervised learning methods.

本研究介绍了用于事件相机的第一个非常大的检测数据集，包含超过 39 小时的汽车记录，涵盖城市，高速公路，郊区和乡村场景等多个不同的驾驶场景和各种天气和照明条件。该数据集的可用性将有助于事件感知视觉任务的主要进步，例如目标检测和分类，并期望在自监督学习方法等其他任务中获得好处。