IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery

• URL: http://arxiv.org/abs/2310.16761v1
• Date: Wed, 25 Oct 2023 16:50:24 GMT
• Title: IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery
• Authors: Bhavuk Singhal, Ashim Gupta, Shivasankaran V P, Amrith Krishna
• Abstract summary: We propose IntenDD, a unified approach leveraging a shared utterance encoding backbone. IntenDD uses an entirely unsupervised contrastive learning strategy for representation learning. We find that our approach consistently outperforms competitive baselines across all three tasks.
• Score: 12.905097743551774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying intents from dialogue utterances forms an integral component of task-oriented dialogue systems. Intent-related tasks are typically formulated either as a classification task, where the utterances are classified into predefined categories or as a clustering task when new and previously unknown intent categories need to be discovered from these utterances. Further, the intent classification may be modeled in a multiclass (MC) or multilabel (ML) setup. While typically these tasks are modeled as separate tasks, we propose IntenDD, a unified approach leveraging a shared utterance encoding backbone. IntenDD uses an entirely unsupervised contrastive learning strategy for representation learning, where pseudo-labels for the unlabeled utterances are generated based on their lexical features. Additionally, we introduce a two-step post-processing setup for the classification tasks using modified adsorption. Here, first, the residuals in the training data are propagated followed by smoothing the labels both modeled in a transductive setting. Through extensive evaluations on various benchmark datasets, we find that our approach consistently outperforms competitive baselines across all three tasks. On average, IntenDD reports percentage improvements of 2.32%, 1.26%, and 1.52% in their respective metrics for few-shot MC, few-shot ML, and the intent discovery tasks respectively.

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