Comparing Regularization Methods for Text Classification with Limited Data

Основні поняття

The author compares the effectiveness of regularization methods on text classification models with limited labeled data, highlighting the impact of simple and complex models when regularized. The study explores how different techniques like adversarial training and semi-supervised learning can improve model performance.

Анотація

The study investigates the impact of regularization methods on text classification models with limited labeled data. It compares simple word embedding-based models to more complex CNN and BiLSTM models, showcasing how regularization techniques enhance performance. The research delves into the importance of regularization in improving model robustness and generalization, especially in scenarios with scarce labeled data. Various methods such as adversarial training and semi-supervised learning are explored to demonstrate their efficacy in enhancing model performance. The study emphasizes the significance of choosing appropriate model formulations and leveraging regularization techniques for effective text classification.

Статистика

In supervised learning, adversarial training can regularize the model further.
We compare simple word embedding-based model which is simple but effective model and complex models (CNN and BiLSTM).
Using only 0.1% 0.5% of its original labelled training documents.
The simple model relatively performs well in fully supervised learning.
Both simple and complex models can be regularized, showing better results for complex models.
Complex model with well-designed priori belief can be also robust to overfit.

Цитати

"Regularization is a technique of increasing performance of a model by reducing overfitting." - Goodfellow et al., 2015
"Adding regularization terms to an objective function provides a priori knowledge about the model." - Bishop, C.M., 2006
"In text classification, we mimic the perturbation by replacing words with 'unknown' tokens or switching the word order in a document." - Miyato et al., 2019

Ключові висновки, отримані з

Comparing effectiveness of regularization methods on text classification

by Jongga Lee,J... о arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.00825.pdf

Глибші Запити

How do different regularization methods impact the interpretability of text classification models?

Regularization methods in text classification can have varying impacts on model interpretability. Techniques like adversarial training, Pi model, and virtual adversarial training are primarily focused on improving generalization and reducing overfitting rather than directly enhancing interpretability. However, by promoting smoother decision boundaries and more robust models, these regularization methods indirectly contribute to better interpretability.
Adversarial training introduces perturbations to inputs during training to enhance model robustness against adversarial attacks but may make it challenging to understand how specific features influence predictions. On the other hand, techniques like Pi model encourage smoothness in output distributions based on input variations without explicitly altering feature importance.
In contrast, simpler models like SWEM (Simple Word Embedding-based Model) may offer more straightforward interpretations due to their less complex structure. These models rely on basic operations like pooling over word embeddings, making it easier to trace back decisions made by the model.
Overall, while advanced regularization methods might not directly enhance interpretability in text classification models, they can indirectly improve it by creating more stable and reliable models that are easier to analyze.

What are potential drawbacks or limitations associated with using simpler models in text classification tasks?

Using simpler models in text classification tasks comes with its own set of drawbacks and limitations:

Limited Representational Power: Simple models like SWEM may struggle with capturing intricate relationships within textual data compared to more complex architectures like CNNs or LSTMs. This limitation can lead to lower performance on tasks requiring nuanced understanding.

Lack of Flexibility: Simpler models often have fixed structures that cannot adapt well to diverse datasets or complex patterns present in natural language data. They might underperform when faced with varied linguistic styles or contexts.

Reduced Performance: While simple models excel at certain tasks due to their efficiency and ease of implementation, they may not achieve state-of-the-art results on challenging benchmarks where sophisticated modeling is required.

Difficulty Handling Long Sequences: Some simple architectures struggle with processing long sequences efficiently due to limitations in memory management or contextual information retention.

Vulnerability to Overfitting: In cases where a dataset is small or highly imbalanced, simpler models might be prone...

How might advancements in transformer architectures influence future research on regularization techniques for text classification?

Advancements in transformer architectures have already revolutionized natural language processing (NLP) tasks by introducing attention mechanisms that capture long-range dependencies effectively across sequences.
These developments will likely shape future research directions regarding regularization techniques for text classification as follows:
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