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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
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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:
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.
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Proceedings ArticleDOI
ReferItGame: Referring to Objects in Photographs of Natural Scenes
TL;DR: A new game to crowd-source natural language referring expressions by designing a two player game that can both collect and verify referring expressions directly within the game and provides an in depth analysis of the resulting dataset.
Proceedings ArticleDOI
Habitat: A Platform for Embodied AI Research
Manolis Savva,Jitendra Malik,Devi Parikh,Dhruv Batra,Abhishek Kadian,Oleksandr Maksymets,Yili Zhao,Erik Wijmans,Bhavana Jain,Julian Straub,Jia Liu,Vladlen Koltun +11 more
TL;DR: The comparison between learning and SLAM approaches from two recent works are revisited and evidence is found -- that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and the first cross-dataset generalization experiments are conducted.
Journal ArticleDOI
A Survey on Automatic Detection of Hate Speech in Text
Paula Fortuna,Sérgio Nunes +1 more
TL;DR: This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used, and provides a unifying definition of hate speech.
Proceedings ArticleDOI
The Risk of Racial Bias in Hate Speech Detection.
TL;DR: This work proposes *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet’s dialect they are significantly less likely to label the tweet as offensive.
Proceedings Article
Visual Dialog
Abhishek Das,Satwik Kottur,Khushi Gupta,Avi Singh,Deshraj Yadav,Jose M. F. Moura,Devi Parikh,Dhruv Batra +7 more
TL;DR: In this article, the authors introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content, given an image, a dialog history and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately.