Generalized Zero-shot Intent Detection via Commonsense Knowledge

A. B. Siddique, Fuad Jamour, Luxun Xu, Vagelis Hristidis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Identifying user intents from natural language utterances is a crucial step in conversational systems that has been extensively studied as a supervised classification problem. However, in practice, new intents emerge after deploying an intent detection model. Thus, these models should seamlessly adapt and classify utterances with both seen and unseen intents - unseen intents emerge after deployment and they do not have training data. The few existing models that target this setting rely heavily on the training data of seen intents and consequently overfit to these intents, resulting in a bias to misclassify utterances with unseen intents into seen ones. We propose RIDE: an intent detection model that leverages commonsense knowledge in an unsupervised fashion to overcome the issue of training data scarcity. RIDE computes robust and generalizable relationship meta-features that capture deep semantic relationships between utterances and intent labels; these features are computed by considering how the concepts in an utterance are linked to those in an intent label via commonsense knowledge. Our extensive experimental analysis on three widely-used intent detection benchmarks shows that relationship meta-features significantly improve the detection of both seen and unseen intents and that RIDE outperforms the state-of-the-art models.

Original languageEnglish
Title of host publicationSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages1925-1929
Number of pages5
ISBN (Electronic)9781450380379
DOIs
StatePublished - Jul 11 2021
Event44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021 - Virtual, Online, Canada
Duration: Jul 11 2021Jul 15 2021

Publication series

NameSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021
Country/TerritoryCanada
CityVirtual, Online
Period7/11/217/15/21

Bibliographical note

Publisher Copyright:
© 2021 Owner/Author.

Keywords

  • generalized zero-shot learning
  • natural language processing
  • natural language understanding
  • out of domain intent detection

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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