IntenDD: A Unified Contrastive Learning Approach for Intent Detection
  and Discovery
Bhavuk Singhal Ashim Gupta Shivasankaran V P Amrith Krishna
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.