conftrace_
2020 AACL AACL 2020

Intent Detection with WikiHow

Abstract

AbstractModern task-oriented dialog systems need to reliably understand usersโ€™ intents. Intent detection is even more challenging when moving to new domains or new languages, since there is little annotated data. To address this challenge, we present a suite of pretrained intent detection models which can predict a broad range of intended goals from many actions because they are trained on wikiHow, a comprehensive instructional website. Our models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. Our models also demonstrate strong zero- and few-shot performance, reaching over 75% accuracy using only 100 training examples in all datasets.

๐Ÿš€ Conference Pioneer - AACL 2020
๐ŸŒ‰ Interdisciplinary Bridge - Machine Learning and Natural Language Processing
๐Ÿฃ Hot Topic Early Bird - zero-shot learning
๐Ÿ Cross-Pollinator - Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio