Intent Detection via Two-Stage Contrastive Pre-training
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Solution Overview
Problem
Conventional intent detection systems in natural language processing require large training datasets and struggle with accurately identifying finely differentiated intents due to semantic similarity, leading to costly and computationally expensive data annotation and limited scalability.
Innovation Solution
A two-stage contrastive pre-training and fine-tuning learning process for neural networks that uses self-supervised and supervised learning methods to effectively train on limited and semantically similar datasets, enabling accurate intent recognition without extensive labeled examples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional intent detection systems use large training datasets, then model accuracy improves, but data annotation cost and computational expense increase
Solution Approach 1:
The system performs pre-training on general language tasks before fine-tuning on specific intent detection tasks. This preliminary action allows the model to learn general language representations that transfer to intent detection, reducing the need for large task-specific training datasets while maintaining accuracy.
Solution Approach 2:
The system changes the training paradigm from direct supervised learning to contrastive learning with information bottleneck regularization. This parameter change in the learning objective allows the model to learn discriminative features with fewer training examples by focusing on preserving essential information while discarding noise.
2Manufacturing precision
If conventional systems train on fine-grained semantically similar examples, then model specificity improves, but training time and annotation cost increase
Solution Approach 1:
The system applies information bottleneck regularization that controls the amount of information preserved in hidden representations. This parameter control allows the model to learn fine-grained distinctions between semantically similar intents while filtering out redundant information, achieving high precision with fewer annotated examples.
Solution Approach 2:
The contrastive learning objective acts as an intermediary mechanism that guides the model to focus on discriminative features. By contrasting positive and negative samples, the system learns to differentiate fine-grained intents without requiring extensive manual annotation of each subtle distinction.
3Reliability
If conventional intent detection systems use more training examples, then model robustness improves, but computational resources and training time increase
Solution Approach 1:
The pre-training stage serves as a preliminary action that establishes robust language representations before task-specific fine-tuning. This two-stage approach allows the model to achieve robustness efficiently by leveraging general language knowledge acquired during pre-training, reducing the computational burden of training on large task-specific datasets.
Solution Approach 2:
The information bottleneck regularization parameter controls the trade-off between representation fidelity and compression. By optimizing this parameter, the system achieves robust generalization with fewer training examples, reducing computational energy consumption while maintaining model reliability.
Data Source
AI summary
Systems and methods for natural language processing are described. One or more aspects of a method, apparatus, and non-transitory computer readable medium include receiving a text phrase; encoding the text phrase using an encoder to obtain a hidden representation of the text phrase, wherein the encoder is trained during a first training phrase using self-supervised learning based on a first contrastive loss and during a second training phrase using supervised learning based on a second contrastive learning loss; identifying an intent of the text phrase from a predetermined set of intent labels using a classification network, wherein the classification network is jointly trained with the encoder in the second training phase; and generating a response to the text phrase based on the intent.