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        <title>Semi-supervised Intent Discovery with Contrastive Learning</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Semi-supervised Intent Discovery with Contrastive Learning
%A Shen, Xiang
%A Sun, Yinge
%A Zhang, Yao
%A Najmabadi, Mani
%Y Papangelis, Alexandros
%Y Budzianowski, Paweł
%Y Liu, Bing
%Y Nouri, Elnaz
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%S Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F shen-etal-2021-semi
%X 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.
%R 10.18653/v1/2021.nlp4convai-1.12
%U https://aclanthology.org/2021.nlp4convai-1.12
%U https://doi.org/10.18653/v1/2021.nlp4convai-1.12
%P 120-129

Markdown (Informal)

[Semi-supervised Intent Discovery with Contrastive Learning](https://aclanthology.org/2021.nlp4convai-1.12) (Shen et al., NLP4ConvAI 2021)

• Semi-supervised Intent Discovery with Contrastive Learning (Shen et al., NLP4ConvAI 2021)

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.