@inproceedings{shen-etal-2021-semi,
title = "Semi-supervised Intent Discovery with Contrastive Learning",
author = "Shen, Xiang and
Sun, Yinge and
Zhang, Yao and
Najmabadi, Mani",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4convai-1.12",
doi = "10.18653/v1/2021.nlp4convai-1.12",
pages = "120--129",
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.",
}
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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
%S Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
%D 2021
%8 nov
%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)
ACL