Robust New Intent Discovery with Reliable Pseudo-Labels and Cluster-Friendly Representations

Core Concepts

A robust new intent discovery framework that generates reliable pseudo-labels through optimal transport and learns cluster-friendly representations with intra-cluster and inter-cluster contrastive learning to effectively distinguish known and novel intents.

Abstract

The paper proposes a Robust New Intent Discovery (RoNID) framework to address the challenges in current new intent discovery (NID) methods, which face issues with inaccurate pseudo-labels and poor representation learning.
The key components of RoNID are:

Reliable Pseudo-label Generation Module:

Formulates the pseudo-label assignment as an optimal transport problem to generate reliable synthetic labels.
Utilizes the Sinkhorn-Knopp algorithm to optimize the objective and match the estimated class distribution.
Dynamically updates the estimated class distribution to prevent cluster degeneration.

Cluster-friendly Representation Learning Module:

Incorporates both intra-cluster and inter-cluster contrastive learning objectives.
Intra-cluster contrastive learning enforces strong compactness within clusters.
Inter-cluster contrastive learning maximizes the distance between cluster prototypes to achieve better separation.
The two modules cooperate iteratively to boost each other's performance.

The experiments on three challenging benchmarks demonstrate that RoNID outperforms previous state-of-the-art methods by a large margin, establishing a new state-of-the-art performance on the NID task.

Stats

The number of known and novel intent classes is denoted as Ck and Cn, respectively, where K = Ck + Cn is the total number of classes.
The dataset consists of a labeled known classes set DL and an unlabeled set DU containing both known and novel intent classes.

Quotes

"To the best of our knowledge, our work is pioneering in the exploration of generating reliable pseudo-labels by addressing an optimal transport problem in the NID task, which is crucial for providing high-quality supervised signals."
"We propose an EM-optimized RoNID framework for the NID problem, which iteratively enhances pseudo-labels generation and representation learning to ensure cluster-friendly intent representations."
"Extensive experiments on three challenging benchmarks show that our model establishes a new state-of-the-art performance on the NID task (average 1.5% improvement), which confirms the effectiveness of RoNID."

Key Insights Distilled From

RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations

by Shun Zhang,C... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08977.pdf

Deeper Inquiries

How can the proposed RoNID framework be extended to handle dynamic changes in the intent space, where new intents continuously emerge over time

The RoNID framework can be extended to handle dynamic changes in the intent space by incorporating a continual learning approach. Continual learning allows the model to adapt to new intents that emerge over time without forgetting previously learned knowledge. One way to achieve this is by implementing a replay mechanism that stores past data samples and periodically revisits them during training to prevent catastrophic forgetting. Additionally, the model can be augmented with a mechanism to detect and adapt to concept drift, where the underlying data distribution shifts over time. By continuously updating the model with new data and adjusting the representation learning process, RoNID can effectively handle the dynamic nature of the intent space.

What are the potential limitations of the optimal transport-based pseudo-label generation approach, and how can it be further improved to handle more complex data distributions

The optimal transport-based pseudo-label generation approach may face limitations in handling more complex data distributions due to its sensitivity to outliers and high computational complexity. To address these limitations, several improvements can be considered. One approach is to incorporate robust optimization techniques to make the pseudo-label generation process more resilient to outliers and noisy data. Additionally, exploring different cost functions and regularization techniques in the optimal transport formulation can help adapt to diverse data distributions. Furthermore, integrating uncertainty estimation methods can provide a measure of confidence in the generated pseudo-labels, enhancing the robustness of the approach in handling complex data distributions.

Can the cluster-friendly representation learning module be adapted to other tasks beyond new intent discovery, such as open-set recognition or few-shot learning, and what are the potential challenges in doing so

The cluster-friendly representation learning module can be adapted to other tasks beyond new intent discovery, such as open-set recognition or few-shot learning, by modifying the contrastive learning objectives to suit the specific requirements of the task. For open-set recognition, the representation learning module can be tailored to encourage the model to learn representations that effectively separate known classes from unknown classes. This can be achieved by incorporating additional constraints or loss functions that promote better discrimination between known and unknown samples. In the case of few-shot learning, the module can be adapted to facilitate the rapid adaptation of the model to new classes with limited labeled data. Challenges in adapting the module to these tasks may include designing appropriate loss functions, handling class imbalance, and ensuring the generalization of learned representations to unseen classes.