Intent Detection with WikiHow

Li Zhang, Qing Lyu, Chris Callison-Burch


Abstract
Modern task-oriented dialog systems need to reliably understand users’ 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.
Anthology ID:
2020.aacl-main.35
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Month:
December
Year:
2020
Address:
Suzhou, China
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
328–333
Language:
URL:
https://aclanthology.org/2020.aacl-main.35
DOI:
Bibkey:
Cite (ACL):
Li Zhang, Qing Lyu, and Chris Callison-Burch. 2020. Intent Detection with WikiHow. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 328–333, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Intent Detection with WikiHow (Zhang et al., AACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.aacl-main.35.pdf
Code
 zharry29/wikihow-intent
Data
SGDSNIPS