2018
EMNLP
EMNLP 2018
Using context to identify the language of face-saving
Abstract
AbstractWe created a corpus of utterances that attempt to save face from parliamentary debates and use it to automatically analyze the language of reputation defence. Our proposed model that incorporates information regarding threats to reputation can predict reputation defence language with high confidence. Further experiments and evaluations on different datasets show that the model is able to generalize to new utterances and can predict the language of reputation defence in a new dataset.
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— threat detection
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Hot Topic Early Bird
— computational linguistics
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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