Learning Discriminative Representations and Decision Boundaries for Open Intent Detection

TL;DR

This paper introduces DA-ADB, a novel framework that improves open intent detection by learning distance-aware representations and adaptive decision boundaries, leading to superior performance on benchmark datasets.

Contribution

The paper proposes a new framework that effectively learns intent representations and decision boundaries for open intent detection, addressing key challenges in the field.

Findings

Significant performance improvements over state-of-the-art methods

Robustness across different labeled data proportions

Effective detection of unseen open intents

Abstract

Open intent detection is a significant problem in natural language understanding, which aims to identify the unseen open intent while ensuring known intent identification performance. However, current methods face two major challenges. Firstly, they struggle to learn friendly representations to detect the open intent with prior knowledge of only known intents. Secondly, there is a lack of an effective approach to obtaining specific and compact decision boundaries for known intents. To address these issues, this paper presents an original framework called DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection. Specifically, we first leverage distance information to enhance the distinguishing capability of the intent representations. Then, we design a novel loss function to obtain appropriate decision boundaries by balancing both empirical and open space risks. Extensive experiments demonstrate the effectiveness of the proposed distance-aware and boundary learning strategies. Compared to state-of-the-art methods, our framework achieves substantial improvements on three benchmark datasets. Furthermore, it yields robust performance with varying proportions of labeled data and known categories.