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2019 EMNLP EMNLP 2019

Multilingual and Multi-Aspect Hate Speech Analysis

Abstract

AbstractCurrent research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual multi-aspect hate speech analysis dataset and use it to test the current state-of-the-art multilingual multitask learning approaches. We evaluate our dataset in various classification settings, then we discuss how to leverage our annotations in order to improve hate speech detection and classification in general.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — multi-aspect classification
🐣 Hot Topic Early Bird — multilingual model
🐝 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

Authors

Nedjma Ousidhoum , Zizheng Lin , Hongming Zhang , Yangqiu Song , Dit-Yan Yeung

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification Natural Language Processing > Applications > Text Classification Machine Learning > Learning Types > Multi-Task Learning Machine Learning > Learning Types > Multi-Lingual Learning

Keywords

multi-task learning transfer learning text classification multilingual nlp multilingual model multilingual learning hate speech detection multi-aspect classification multitask approach annotation granularity
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