Hate speech detection is a critical problem in social media platforms, being
often accused for enabling the spread of hatred and igniting physical violence.
Hate speech detection requires overwhelming resources including
high-performance computing for online posts and tweets monitoring as well as
thousands of human experts for daily screening of suspected posts or tweets.
Recently, Deep Learning (DL)-based solutions have been proposed for automatic
detection of hate speech, using modest-sized training datasets of few thousands
of hate speech sequences. While these methods perform well on the specific
datasets, their ability to detect new hate speech sequences is limited and has
not been investigated. Being a data-driven approach, it is well known that DL
surpasses other methods whenever a scale-up in train dataset size and diversity
is achieved. Therefore, we first present a dataset of 1 million realistic hate
and non-hate sequences, produced by a deep generative language model. We
further utilize the generated dataset to train a well-studied DL-based hate
speech detector, and demonstrate consistent and significant performance
improvements across five public hate speech datasets. Therefore, the proposed
solution enables high sensitivity detection of a very large variety of hate
speech sequences, paving the way to a fully automatic solution.

该研究探讨使用深度学习对 hate speech 进行自动检测，基于生成的 1 百万条数据集进行模型训练，在公共 hate speech 数据集上获得了显著的性能提升，提高了对多样的 hate speech 序列的高灵敏检测能力，为实现完全自动化的解决方案铺平了道路。

利用深度生成模型实现大规模仇恨言论检测

Towards Hate Speech Detection at Large via Deep Generative Modeling

Temporal common sense has applications in AI tasks such as QA, multi-document
summarization, and human-AI communication. We propose the task of sequencing --
given a jumbled set of aligned image-caption pairs that belong to a story, the
task is to sort them such that the output sequence forms a coherent story. We
present multiple approaches, via unary (position) and pairwise (order)
predictions, and their ensemble-based combinations, achieving strong results on
this task. We use both text-based and image-based features, which depict
complementary improvements. Using qualitative examples, we demonstrate that our
models have learnt interesting aspects of temporal common sense.

给定图像与描述文本，使用排序方法对其进行一致性的故事串联。通过多种方法设计组合模型，使用了基于文本和图像特征的成分。模型具备了学会了时间共识的有趣方面的能力。

混乱的图片和标题排序故事

Sort Story: Sorting Jumbled Images and Captions into Stories

Motivation: The MinION device by Oxford Nanopore is the first portable
sequencing device. MinION is able to produce very long reads (reads over
100~kBp were reported), however it suffers from high sequencing error rate. In
this paper, we show that the error rate can be reduced by improving the base
calling process.
Results: We present the first open-source DNA base caller for the MinION
sequencing platform by Oxford Nanopore. By employing carefully crafted
recurrent neural networks, our tool improves the base calling accuracy compared
to the default base caller supplied by the manufacturer. This advance may
further enhance applicability of MinION for genome sequencing and various
clinical applications.
Availability: DeepNano can be downloaded at
this http URL
Contact: boza@fmph.uniba.sk

本文介绍了 DeepNano，一种用于改进 MinION 测序平台 DNA 碱基呼叫的工具，使用精心设计的递归神经网络，与制造商提供的默认碱基呼叫者相比，我们的工具提高了碱基呼叫的准确性，这一进步可能进一步提高 MinION 在基因组测序和各种临床应用方面的适用性。