Semi-supervised Intent Discovery with Contrastive Learning

Xiang Shen, Yinge Sun, Yao Zhang, Mani Najmabadi


Abstract
User intent discovery is a key step in developing a Natural Language Understanding (NLU) module at the core of any modern Conversational AI system. Typically, human experts review a representative sample of user input data to discover new intents, which is subjective, costly, and error-prone. In this work, we aim to assist the NLU developers by presenting a novel method for discovering new intents at scale given a corpus of utterances. Our method utilizes supervised contrastive learning to leverage information from a domain-relevant, already labeled dataset and identifies new intents in the corpus at hand using unsupervised K-means clustering. Our method outperforms the state-of-the-art by a large margin up to 2% and 13% on two benchmark datasets, measured by clustering accuracy. Furthermore, we apply our method on a large dataset from the travel domain to demonstrate its effectiveness on a real-world use case.
Anthology ID:
2021.nlp4convai-1.12
Volume:
Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
Month:
November
Year:
2021
Address:
Online
Venues:
EMNLP | NLP4ConvAI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
120–129
Language:
URL:
https://aclanthology.org/2021.nlp4convai-1.12
DOI:
10.18653/v1/2021.nlp4convai-1.12
Bibkey:
Cite (ACL):
Xiang Shen, Yinge Sun, Yao Zhang, and Mani Najmabadi. 2021. Semi-supervised Intent Discovery with Contrastive Learning. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI, pages 120–129, Online. Association for Computational Linguistics.
Cite (Informal):
Semi-supervised Intent Discovery with Contrastive Learning (Shen et al., NLP4ConvAI 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nlp4convai-1.12.pdf