PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks (2202.12499v2)

Abstract: This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based D}ata Augmentation model (PromDA) which only trains small-scale Soft Prompt (i.e., a set of trainable vectors) in the frozen Pre-trained LLMs (PLMs). This avoids human effort in collecting unlabeled in-domain data and maintains the quality of generated synthetic data. In addition, PromDA generates synthetic data via two different views and filters out the low-quality data using NLU models. Experiments on four benchmarks show that synthetic data produced by PromDA successfully boost up the performance of NLU models which consistently outperform several competitive baseline models, including a state-of-the-art semi-supervised model using unlabeled in-domain data. The synthetic data from PromDA are also complementary with unlabeled in-domain data. The NLU models can be further improved when they are combined for training.

A Comprehensive Overview of PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks

The paper "PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks" by Wang et al., introduces an innovative method for enhancing Natural Language Understanding (NLU) models, particularly within low-resource environments. It addresses a notable challenge in deep learning: the high demand for large-scale labeled training data, which is often infeasible to amass in many low-resource contexts such as sentence classification and sequence labeling tasks. Traditional augmentation methods, while useful, often produce syntactically or semantically distorted data, reducing their efficacy.

Key Contributions

At the core of this paper lies the PromDA model, a Prompt-based Data Augmentation approach that utilizes prompt tuning within Pre-trained LLMs (PLMs) to generate high-quality synthetic data efficiently. This method hinges on the following insights and methodologies:

1. Soft Prompt Tuning: PromDA adopts a novel technique by freezing the entire pre-trained model while only tuning small-scale soft prompts—trainable vectors added to the input. This minimizes risk of overfitting typical to fine-tuning on small datasets.
2. Dual-View Generation: Synthetic data is generated from two perspectives: an Output View, leveraging output tags, and an Input View, utilizing input keywords. This duality enriches the diversity of generated samples compared to solely focusing on a single source of input information.
3. NLU Consistency Filtering: The filtering process ensures that only high-quality synthetic examples, verified consistent with NLU model predictions, are used for model training. This iterative filtering and training process leads to progressively stronger NLU models.
4. Prompt Initialization Strategy: To tackle prompt initialization, the authors employ a pre-training task wherein synonym keywords are converted back to sentences. This task fine-tunes prompts within the frozen PLMs, ensuring a rich initialization that aids in generating novel samples while maintaining coherence.

Experimental Validation

The effectiveness of PromDA is demonstrated through experiments across four benchmarks: CoNLL03, Wikiann, SST-2, and RT. The results consistently show that NLU models trained with PromDA-synthesized data outperform several competitive baselines, including robust semi-supervised approaches that rely on unlabeled in-domain data. This not only highlights the quality of data generated by PromDA but also suggests its complementary nature when integrated with these traditional methods. The dual-view approach and soft prompt tuning yield superior diversity and generalization capabilities.

Theoretical and Practical Implications

From a theoretical standpoint, PromDA illustrates the potential of prompt-based learning within pre-trained models for data augmentation tasks, particularly in low-resource scenarios where traditional methods struggle. Practically, it provides a framework for improving NLU system performance without the burdensome requirement of large annotated datasets. The model's success in outperforming even state-of-the-art self-training methods denotes its capability in extracting implicit knowledge from PLMs, crucial for generating rich, informative examples.

Future Prospects

Looking forward, the potential of PromDA can be further extended to other NLP tasks, such as question answering and text generation, which could similarly benefit from its underlying principles. Further research could explore integrating more sophisticated semi-supervised learning techniques with PromDA to unveil additional layers of performance improvements.

In summary, PromDA establishes itself as a proficient approach to data augmentation for NLU, adeptly boosting model performance in data-scarce environments. Its reliance on prompt-based techniques marks a paradigm shift from conventional augmentation strategies, promising both theoretical insights and practical advancements in the field of natural language processing.