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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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VQA: Visual Question Answering
Stanislaw Antol,Aishwarya Agrawal,Jiasen Lu,Margaret Mitchell,Dhruv Batra,C. Lawrence Zitnick,Devi Parikh +6 more
TL;DR: The task of free-form and open-ended Visual Question Answering (VQA) is proposed, given an image and a natural language question about the image, the task is to provide an accurate natural language answer.
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CIDEr: Consensus-based image description evaluation
TL;DR: A novel paradigm for evaluating image descriptions that uses human consensus is proposed and a new automated metric that captures human judgment of consensus better than existing metrics across sentences generated by various sources is evaluated.
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HuggingFace's Transformers: State-of-the-art Natural Language Processing.
Thomas Wolf,Lysandre Debut,Victor Sanh,Julien Chaumond,Clement Delangue,Anthony Moi,Pierric Cistac,Tim Rault,Rémi Louf,Morgan Funtowicz,Jamie Brew +10 more
TL;DR: The \textit{Transformers} library is an open-source library that consists of carefully engineered state-of-the art Transformer architectures under a unified API and a curated collection of pretrained models made by and available for the community.
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Aggregated Residual Transformations for Deep Neural Networks
TL;DR: On the ImageNet-1K dataset, it is empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy and is more effective than going deeper or wider when the authors increase the capacity.
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VQA: Visual Question Answering
Aishwarya Agrawal,Jiasen Lu,Stanislaw Antol,Margaret Mitchell,C. Lawrence Zitnick,Dhruv Batra,Devi Parikh +6 more
TL;DR: The task of free-form and open-ended Visual Question Answering (VQA) is proposed, given an image and a natural language question about the image, the task is to provide an accurate natural language answer.