Less is More: Parameter-Free Text Classification with Gzip (2212.09410v1)

Abstract: Deep neural networks (DNNs) are often used for text classification tasks as they usually achieve high levels of accuracy. However, DNNs can be computationally intensive with billions of parameters and large amounts of labeled data, which can make them expensive to use, to optimize and to transfer to out-of-distribution (OOD) cases in practice. In this paper, we propose a non-parametric alternative to DNNs that's easy, light-weight and universal in text classification: a combination of a simple compressor like gzip with a $k$-nearest-neighbor classifier. Without any training, pre-training or fine-tuning, our method achieves results that are competitive with non-pretrained deep learning methods on six in-distributed datasets. It even outperforms BERT on all five OOD datasets, including four low-resource languages. Our method also performs particularly well in few-shot settings where labeled data are too scarce for DNNs to achieve a satisfying accuracy.

• The paper introduces a novel parameter-free approach that leverages gzip to approximate Kolmogorov complexity for effective text classification.
• It employs the Normalized Compression Distance with a kNN classifier, achieving competitive results against deep neural networks on various datasets.
• The study highlights the method's strength in few-shot learning and low-resource language scenarios, offering a resource-efficient alternative to DNNs.

Essay: Parameter-Free Text Classification with Gzip

The paper "Less is More: Parameter-Free Text Classification with Gzip" presents a novel non-parametric approach to text classification that combines a simple compressor, gzip, with a $k$-nearest-neighbor ($k$NN) classifier. The proposed method is positioned as an alternative to deep neural networks (DNNs), which, while effective, are computationally demanding due to their extensive parameter sets and training requirements.

Research Overview

Text classification, a core task in NLP, typically leverages DNNs due to their ability to learn complex patterns. However, these models are data-intensive and necessitate significant computational resources for training and inference. The authors introduce a lightweight approach without the need for training or complex preprocessing. By using a lossless compressor like gzip, the method capitalizes on the notion that objects from the same category share regularities that can be effectively captured through compression.

Central to this method is the Normalized Compression Distance (NCD), which approximates the theoretical framework of Kolmogorov complexity to measure the similarity between text instances. The paper demonstrates that the gzip-based approach achieves competitive results against non-pretrained models on six in-distribution datasets and exceeds the performance of BERT on five out-of-distribution datasets, particularly in low-resource language scenarios.

Strong Empirical Results

The method's empirical evaluation across several datasets shows its robustness and applicability. Notably, the gzip-based classifier shows strong performance on:

• Out-of-Distribution Datasets: Surpassing BERT on datasets in non-English languages (e.g., Kinyarwanda, Kirundi), the gzip method underscores its effectiveness in abstractive classification tasks where pre-trained models typically face challenges due to limited prior exposure.
• Few-Shot Learning: The method shines in few-shot settings, highlighting its potential as a practical tool in scenarios with limited labeled data. It outperforms several traditional and pre-trained models on few-shot tasks, evidencing the efficiency of compressor-based metrics in capturing class-specific regularities with minimal examples.

Implications and Future Directions

This parameter-free strategy highlights a significant shift from model-centric approaches to algorithmic simplicity. The gzip method offers a refreshing alternative that could lead to broader applications in environments constrained by computational resources. Furthermore, the paper opens avenues for integrating modern neural compressors, potentially enhancing classification performance through better approximation of Kolmogorov complexity.

Future work might explore the synergy between neural and traditional compression techniques, optimizing compressors as feature extractors in the NLP pipeline. With the escalating demand for adaptable AI solutions across diverse linguistic landscapes, such innovations could democratize access to sophisticated text classification capabilities without the prohibitive costs associated with training and maintaining large neural models.

In conclusion, the proposed method underscores the pragmatic minimalism where simplicity yields significant returns, challenging the preeminence of data-hungry DNNs in text classification tasks.