CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding (2010.08670v1)

Abstract: Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, designing label-preserving transformations for text data tends to be more challenging. In this paper, we propose a novel data augmentation framework dubbed CoDA, which synthesizes diverse and informative augmented examples by integrating multiple transformations organically. Moreover, a contrastive regularization objective is introduced to capture the global relationship among all the data samples. A momentum encoder along with a memory bank is further leveraged to better estimate the contrastive loss. To verify the effectiveness of the proposed framework, we apply CoDA to Transformer-based models on a wide range of natural language understanding tasks. On the GLUE benchmark, CoDA gives rise to an average improvement of 2.2% while applied to the RoBERTa-large model. More importantly, it consistently exhibits stronger results relative to several competitive data augmentation and adversarial training base-lines (including the low-resource settings). Extensive experiments show that the proposed contrastive objective can be flexibly combined with various data augmentation approaches to further boost their performance, highlighting the wide applicability of the CoDA framework.

An Analysis of CoDA: A Novel Framework for Data Augmentation in Natural Language Processing

The paper "CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding" introduces an innovative framework designed to tackle challenges associated with data augmentation in natural language processing. This novel approach integrates multiple transformations to generate diverse and informative augmented examples and employs a contrastive regularization objective to capture global relationships among data samples.

Overview of the CoDA Framework

Natural language understanding often faces difficulties associated with the discrete nature of textual data, where designing effective label-preserving transformations is inherently complex. Traditional augmentation methods generally operate in isolation, focusing primarily on enhancing model robustness and generalization by increasing annotated sample availability.

CoDA introduces a layered approach to data augmentation, integrating both back-translation and adversarial training techniques. By stacking adversarial training atop back-translation, this framework succeeds in yielding more diverse and informative samples. The augmented examples deviate significantly from their original counterparts, thus fostering enhanced generalization capabilities in models trained on augmented data.

Technical Deep Dive

The authors explore various strategies to combine transformations, such as random combinations, mixup interpolation, and sequential stacking. Experimental results suggest that sequential stacking—particularly the sequence of back-translation followed by adversarial training—produces superior augmented samples relative to other combinations.

Central to CoDA's innovation is its use of a contrastive learning objective, which is adept at capturing global relationships among data samples. This contrasts with the more typical localized consistency-based training that ensures only the augmented samples maintain their original semantics. The contrastive learning component, complemented by a momentum encoder and memory bank, enables effective model training by maintaining relationships between augmented samples and their original counterparts across the entire dataset.

Empirical Validation

Through extensive experimentation on the GLUE benchmark suite, CoDA demonstrates significant performance improvements across multiple tasks. Notably, the framework consistently outperforms competitive baselines predicated on either solitary data augmentation or adversarial training. Analysis reveals CoDA’s pronounced effectiveness on datasets with limited training examples, suggesting its potential utility in low-resource settings.

Implications and Future Directions

The implications of CoDA are both practical and theoretical. Practically, the methodology holds promise for enhancing NLU systems, particularly where annotated data is scarce. Theoretically, CoDA’s success in integrating contrastive objectives into NLP tasks highlights an area ripe for further exploration. The adaptability of contrastive regularization indicates a potential paradigm shift in how augmented data can be leveraged for training robust models.

Looking forward, future research may concentrate on the versatile applicability of CoDA to other NLP tasks beyond classification, potentially expanding its utility in fields where data diversity and augmentation are paramount. Furthermore, integrating this framework into LLM pre-training could open avenues for substantial advancements in model comprehension and synthesis abilities.

In conclusion, CoDA stands as a substantial contribution to the field of data augmentation within natural language processing. Its structured integration of diverse transformations, paired with holistic data relationship modeling, addresses pivotal challenges and sets the stage for continued innovation in enhancing model performance and generalization through strategic data augmentation techniques.