@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",
    editor = "Papangelis, Alexandros  and
      Budzianowski, Pawe{\l}  and
      Liu, Bing  and
      Nouri, Elnaz  and
      Rastogi, Abhinav  and
      Chen, Yun-Nung",
    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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}