2017
EMNLP
EMNLP 2017
Deeper Attention to Abusive User Content Moderation
Abstract
AbstractExperimenting with a new dataset of 1.6M user comments from a news portal and an existing dataset of 115K Wikipedia talk page comments, we show that an RNN operating on word embeddings outpeforms the previous state of the art in moderation, which used logistic regression or an MLP classifier with character or word n-grams. We also compare against a CNN operating on word embeddings, and a word-list baseline. A novel, deep, classificationspecific attention mechanism improves the performance of the RNN further, and can also highlight suspicious words for free, without including highlighted words in the training data. We consider both fully automatic and semi-automatic moderation.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— content moderation
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Hot Topic Early Bird
— content moderation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Core Methods > Classification
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Applications > Sentiment Analysis
Machine Learning > Learning Types > Classification
Deep Learning > Learning Types > Deep Learning
Deep Learning > Techniques > Attention