Induction Networks for Few-Shot Text Classification (1902.10482v2)

Abstract: Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a small support set at the sample-wise level. However, this sample-wise comparison may be severely disturbed by the various expressions in the same class. Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries. In this paper, we propose a novel Induction Network to learn such a generalized class-wise representation, by innovatively leveraging the dynamic routing algorithm in meta-learning. In this way, we find the model is able to induce and generalize better. We evaluate the proposed model on a well-studied sentiment classification dataset (English) and a real-world dialogue intent classification dataset (Chinese). Experiment results show that on both datasets, the proposed model significantly outperforms the existing state-of-the-art approaches, proving the effectiveness of class-wise generalization in few-shot text classification.

• The paper introduces Induction Networks, employing a dynamic routing method to induce robust class representations from minimal labeled data for few-shot text classification.
• Empirical evaluation shows that Induction Networks achieve significant accuracy improvements over state-of-the-art methods on two datasets, especially under high-variance conditions.
• This approach improves the feasibility of deploying AI models in real-world applications where collecting large labeled datasets is challenging, such as specialized sentiment analysis or chatbot systems.

Induction Networks for Few-Shot Text Classification

The paper "Induction Networks for Few-Shot Text Classification" presents an innovative approach to address the challenge of text classification in scenarios where data is scarce, or adaptation to unseen classes is necessary. Conventional text classification methods, typically reliant on ample labeled data, often falter in these contexts. To mitigate this issue, the authors introduce the concept of Induction Networks, which leverage a class-wise dynamic routing method to improve few-shot learning performance.

Core Contributions

Induction Networks aim to effectively generalize class-level representations from limited data in the support set, which is crucial for few-shot learning tasks. The proposed model comprises three modules: the Encoder Module, Induction Module, and Relation Module.

1. Encoder Module: This employs a bidirectional LSTM with self-attention, generating embeddings that summarize the semantic content of text inputs.
2. Induction Module: The heart of the innovation, this module uses the dynamic routing algorithm to create robust class representations. By mapping sample vectors to class vectors while dynamically adjusting coupling coefficients through a non-linear transformation, it encapsulates class semantics effectively.
3. Relation Module: Aims to measure the correlation between queries and classes using a neural tensor layer, providing a scalar similarity score.

Implementation and Results

The research evaluates the Induction Networks approach on two datasets: the Amazon Review Sentiment Classification (ARSC) and the Open Domain Intent Classification for Dialog Systems (ODIC). The empirical results demonstrate significant improvements in mean accuracy over existing state-of-the-art few-shot text classification models, particularly in high-variance conditions where previous methods were prone to sample-level noise.

Implications and Future Directions

The introduction of Induction Networks posits a notable direction in few-shot learning, particularly in the domain of text classification. The ability to induce generalized class-level features from minimal labeled data increases the practicality of deploying AI models in real-world applications with limited data availability, such as customer service chatbots and domain-specific sentiment analysis tasks.

The authors suggest that future research could explore refining the dynamic routing mechanism to further enhance its capacity for generalization. Additionally, extending this framework to other modalities in machine learning beyond text, such as vision or audio classification, presents intriguing possibilities. Further investigation into hybrid models that combine induction networks with other few-shot learning techniques could also yield substantial insights, potentially leading to even more robust performance.

In summary, the paper provides a strong foundation for future research in few-shot learning, with Induction Networks opening pathways for practical applications where data collection remains a significant barrier. The combination of meta-learning and dynamic routing within this framework advances the field's understanding of how to encapsulate class-level semantics effectively, offering robust solutions to longstanding challenges in AI and machine learning.