New Intent Discovery with Robust and Adaptive Prototypical Learning Framework

Alapfogalmak

Robust and Adaptive Prototypical Learning Framework for New Intent Discovery.

Kivonat

The article introduces a Robust and Adaptive Prototypical Learning (RAP) framework for New Intent Discovery (NID). It addresses the limitations of existing methods by focusing on within-cluster compactness and between-cluster separation. The RAP framework consists of Robust Prototypical Attracting Learning (RPAL) and Adaptive Prototypical Dispersing Learning (APDL) to optimize intent representations. Experimental results show significant improvements over current state-of-the-art methods, even outperforming large language models.
Abstract:

New Intent Discovery aims to recognize known and infer new intent categories.
Existing methods lack cluster-friendly representations.
RAP proposes RPAL and APDL to enhance within-cluster compactness and between-cluster separation.
Experimental results demonstrate substantial improvements over current methods.
Introduction:

Conventional intent detection in dialogue systems focuses on pre-defined intents.
New Intent Discovery is essential for handling new intents outside existing categories.
Early works adopt unsupervised clustering, while recent studies explore semi-supervised settings.
Approach:

Problem Definition: NID follows an open-world setting to recognize all intents with limited labeled data.
Intent Representation Learning: Pre-trained BERT model used for feature extraction and fine-tuning on labeled data.
Categorical Prototypes Generation: Class prototypes computed as representative embeddings within each class.
Robust Prototypical Attracting: RPAL minimizes instance-to-prototype distances for within-cluster compactness.
Adaptive Prototypical Dispersing: APDL maximizes prototype-to-prototype distances for between-cluster dispersion.
Dynamic Prototypes Update: Exponential moving average algorithm used to update class prototypes continuously.
Multitask Learning: Joint optimization of RPAL, APDL, and cross-entropy loss for NID task.

Data Extraction:

"Experimental results evaluated on three challenging benchmarks...average +5.5% improvement."
"Extensive experiments on three benchmark datasets show that our model establishes state-of-the-art performance..."

Statisztikák

Experimental results evaluated on three challenging benchmarks...average +5.5% improvement.

Idézetek

"The proposed RAP significantly outperforms the previous unsupervised and semi-supervised baselines..."
"Our method consistently outperforms ChatGPT3.5 across all datasets..."

Főbb Kivonatok

New Intent Discovery with Attracting and Dispersing Prototype

by Shun Zhang,J... : arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16913.pdf

Mélyebb kérdések

How can the RAP framework be adapted for other machine learning tasks?

The RAP framework's adaptability lies in its core principles of robust and adaptive prototypical learning. These principles can be applied to various machine learning tasks by adjusting the specific objectives and constraints based on the requirements of the task at hand. For instance, in a classification task, the robust prototypical attracting (RPAL) method can be utilized to enhance within-class compactness, while the adaptive prototypical dispersing (APDL) method can focus on maximizing between-class separation. By customizing these components to suit different tasks, the RAP framework can effectively learn cluster-friendly representations for diverse applications.

What are the potential drawbacks of relying on pseudo-labels in the k-means method?

While using pseudo-labels generated by k-means clustering is a common approach in semi-supervised learning, it comes with certain limitations and drawbacks:

Sensitivity to Noise: Pseudo-labels derived from clustering algorithms like k-means are sensitive to noise and outliers in data. This sensitivity can lead to inaccuracies in label assignments.
Lack of Ground Truth: Pseudo-labels do not always reflect true class labels accurately, especially when dealing with complex or overlapping clusters.
Unreliable Training Signal: In cases where pseudo-labels are noisy or incorrect, they may provide an unreliable training signal that hinders model performance.
Limited Generalization: Models trained on pseudo-labeled data may struggle to generalize well beyond the specific dataset used for training.

How can integration of RAP with LLMs further enhance NID performance?

Integrating RAP with Large Language Models (LLMs) has several benefits that can significantly enhance New Intent Discovery (NID) performance:

Improved Representation Learning: LLMs excel at capturing intricate linguistic patterns and semantics from large text corpora. By combining this strength with RAP's cluster-friendly representation learning capabilities, models can better understand user intents even from limited labeled data.
Enhanced Novel Intent Detection: The fusion of RAP's robust and adaptive prototypical learning methods with LLMs' language understanding abilities enables more accurate detection and categorization of novel intents that may not have been seen before.
Increased Model Robustness: Leveraging both frameworks together allows for a more comprehensive approach towards NID tasks, leading to improved model robustness against noisy or ambiguous intent signals.
Interpretable Results: Integrating RAP with LLMs could potentially yield interpretable results where identified intents are not only accurately classified but also presented in a human-understandable format.

By combining these two powerful methodologies, researchers and practitioners could achieve state-of-the-art results in NID while ensuring scalability across different datasets and domains due to their complementary strengths.