Open AccessPosted Content
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Douwe Kiela,Hamed Firooz,Aravind Mohan,Vedanuj Goswami,Amanpreet Singh,Pratik Ringshia,Davide Testuggine +6 more
TLDR
The authors proposed a new challenge set for multimodal classification, focusing on detecting hate speech in multi-modal memes, where difficult examples are added to the dataset to make it hard to rely on unimodal signals.Abstract:Â
This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle and only multimodal models can succeed: difficult examples ("benign confounders") are added to the dataset to make it hard to rely on unimodal signals. The task requires subtle reasoning, yet is straightforward to evaluate as a binary classification problem. We provide baseline performance numbers for unimodal models, as well as for multimodal models with various degrees of sophistication. We find that state-of-the-art methods perform poorly compared to humans (64.73% vs. 84.7% accuracy), illustrating the difficulty of the task and highlighting the challenge that this important problem poses to the community.read more
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Learning Transferable Visual Models From Natural Language Supervision
Alec Radford,Jong Wook Kim,Chris Hallacy,Aditya Ramesh,Gabriel Goh,Sandhini Agarwal,Girish Sastry,Amanda Askell,Pamela Mishkin,Jack Clark,Gretchen Krueger,Ilya Sutskever +11 more
TL;DR: In this article, a pre-training task of predicting which caption goes with which image is used to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet.
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Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages
Thomas Mandl,Sandip Modha,Prasenjit Majumder,Daksh Patel,Mohana Dave,Chintak Mandlia,Aditya Patel +6 more
TL;DR: The HASOC track intends to stimulate development in Hate Speech for Hindi, German and English by identifying Hate Speech in Social Media using LSTM networks processing word embedding input.
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Directions in abusive language training data, a systematic review: Garbage in, garbage out.
Bertie Vidgen,Leon Derczynski +1 more
TL;DR: This paper systematically reviews abusive language dataset creation and content in conjunction with an open website for cataloguing abusive language data leading to a synthesis providing evidence-based recommendations for practitioners working with this complex and highly diverse data.
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TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text
TL;DR: TextOCR as discussed by the authors is an arbitrary-shaped scene text detection and recognition with 900k annotated words collected on real images from TextVQA dataset, which can do scene text based reasoning on an image in an end-to-end fashion.
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Tackling Online Abuse: A Survey of Automated Abuse Detection Methods
TL;DR: A comprehensive survey of the methods that have been proposed to date for automated abuse detection in the field of natural language processing (NLP), providing a platform for further development of this area.
References
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Proceedings Article
Automated Hate Speech Detection and the Problem of Offensive Language
TL;DR: This work used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords and labels a sample of these tweets into three categories: those containinghate speech, only offensive language, and those with neither.
Proceedings ArticleDOI
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Zeerak Waseem,Dirk Hovy +1 more
TL;DR: A list of criteria founded in critical race theory is provided, and these are used to annotate a publicly available corpus of more than 16k tweets and present a dictionary based the most indicative words in the data.
Proceedings ArticleDOI
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Justin Johnson,Bharath Hariharan,Laurens van der Maaten,Li Fei-Fei,C. Lawrence Zitnick,Ross Girshick +5 more
TL;DR: In this paper, the authors present a diagnostic dataset that tests a range of visual reasoning abilities and provides insights into their abilities and limitations, and use this dataset to analyze a variety of modern visual reasoning systems.
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CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Justin Johnson,Bharath Hariharan,Laurens van der Maaten,Li Fei-Fei,C. Lawrence Zitnick,Ross Girshick +5 more
TL;DR: This work presents a diagnostic dataset that tests a range of visual reasoning abilities and uses this dataset to analyze a variety of modern visual reasoning systems, providing novel insights into their abilities and limitations.
Journal ArticleDOI
YFCC100M: the new data in multimedia research
Bart Thomee,David A. Shamma,Gerald Friedland,Benjamin Elizalde,Karl Ni,Douglas N. Poland,Damian Borth,Li-Jia Li +7 more
TL;DR: This publicly available curated dataset of almost 100 million photos and videos is free and legal for all.