Boosting Text Augmentation Performance by Mitigating Feature Space Shift with a Hybrid Instance Filtering Framework

Core Concepts

A hybrid instance-filtering framework (BOOSTAUG) based on pre-trained language models can maintain a similar feature space with natural datasets, significantly improving the performance of existing text augmentation methods on large public datasets.

Abstract

The paper proposes a hybrid instance-filtering framework called BOOSTAUG to address the performance drop problem in existing text augmentation methods. The key insights are: Existing text augmentation methods often generate instances with shifted feature spaces, leading to a drop in performance on the augmented data. BOOSTAUG uses pre-trained language models as a powerful instance filter to maintain the feature space, rather than as an augmentor. It consists of four instance filtering strategies: perplexity filtering, confidence ranking, predicted label constraint, and a cross-boosting strategy. Experiments on three classification tasks and nine public datasets show that BOOSTAUG addresses the feature space shift problem and outperforms state-of-the-art text augmentation methods. BOOSTAUG is a universal augmentation instance filter framework that can be easily integrated with existing text augmentation methods to significantly improve their performance on large datasets.

Stats

Existing augmentation methods like EDA generally lose around 2% in aspect-based sentiment classification accuracy. The feature space shift metric shows that BOOSTAUG has the least feature space shift compared to the original testing set, while EDA and MonoAug have larger shifts.

Quotes

"Our research indicates that existing augmentation methods often generate instances with shifted feature spaces, which leads to a drop in performance on the augmented data (for example, EDA generally loses ≈2% in aspect-based sentiment classification)." "BOOSTAUG is transferable to existing text augmentation methods (such as synonym substitution and back translation) and significantly improves the augmentation performance by ≈2 −3% in classification accuracy."

Key Insights Distilled From

BootAug

by Heng Yang,Ke... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2210.02941.pdf

Deeper Inquiries

How can BOOSTAUG be extended to preserve the grammar and syntax of the augmented instances, especially for syntax-sensitive tasks

To extend BOOSTAUG to preserve the grammar and syntax of augmented instances for syntax-sensitive tasks, we can incorporate additional constraints and filters in the instance filtering process. One approach could be to introduce a grammar and syntax checking mechanism in the filtering strategies. This mechanism would evaluate the syntactic correctness of the augmented instances based on predefined grammar rules. Instances that do not adhere to these rules would be filtered out during the augmentation process. Additionally, incorporating syntactic parsing tools or language models specifically trained for syntax analysis can help ensure that the augmented instances maintain proper grammar and syntax. By integrating these components into the instance filtering framework of BOOSTAUG, we can enhance the quality of augmented data for syntax-sensitive tasks.

What are the potential limitations of using pre-trained language models as instance filters, and how can these be addressed

Using pre-trained language models as instance filters may have limitations related to domain-specific knowledge, bias, and interpretability. One potential limitation is the lack of domain-specific knowledge in pre-trained models, which can lead to filtering out valid instances that contain domain-specific terminology or context. To address this, domain adaptation techniques can be employed to fine-tune the language model on task-specific data, enhancing its understanding of domain-specific language. Another limitation is the potential bias present in pre-trained models, which can impact the filtering decisions. Mitigating bias through bias detection algorithms and bias-aware filtering strategies can help address this issue. Additionally, ensuring the interpretability of the filtering decisions by providing transparency into the filtering process and the reasons behind instance rejection can enhance the trustworthiness of the filtering framework.

How can the proposed feature space shift metric be generalized to other data modalities beyond text, such as images or speech, to evaluate the quality of augmented data

The proposed feature space shift metric can be generalized to other data modalities beyond text, such as images or speech, by adapting the concept of feature space analysis to the specific characteristics of each modality. For images, the feature space shift metric can be applied to visual embeddings extracted from pre-trained image models like CNNs or Transformers. The metric can evaluate the similarity of feature distributions between original and augmented images to assess the quality of augmentation. Similarly, for speech data, the metric can analyze acoustic feature representations to measure the shift in feature space caused by augmentation techniques. By customizing the feature space shift metric to the unique properties of different data modalities, it can serve as a valuable tool for evaluating the quality of augmented data across various domains.