Conditional BERT Contextual Augmentation (1812.06705v1)

Abstract: We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by LLM. BERT demonstrates that a deep bidirectional LLM is more powerful than either an unidirectional LLM or the shallow concatenation of a forward and backward model. We retrofit BERT to conditional BERT by introducing a new conditional masked LLM\footnote{The term "conditional masked LLM" appeared once in original BERT paper, which indicates context-conditional, is equivalent to term "masked LLM". In our paper, "conditional masked LLM" indicates we apply extra label-conditional constraint to the "masked LLM".} task. The well trained conditional BERT can be applied to enhance contextual augmentation. Experiments on six various different text classification tasks show that our method can be easily applied to both convolutional or recurrent neural networks classifier to obtain obvious improvement.

Conditional BERT Contextual Augmentation

The paper "Conditional BERT Contextual Augmentation" by Wu et al. focuses on the development of a novel data augmentation method tailored for labeled sentence datasets. The concept of data augmentation is utilized to enhance the robustness and generalization abilities of deep neural network models, which are often prone to overfitting due to limited training data. Traditional data augmentation methods, well-explored in fields like speech and computer vision, struggle with the semantic invariance and label consistency when applied to text. This paper addresses these challenges by leveraging the capabilities of pre-trained LLMs, specifically BERT.

Key Insights and Contributions

The paper introduces conditional BERT contextual augmentation, which extends the existing contextual augmentation techniques by incorporating label information into the BERT model. BERT, known for its bidirectional language representation, is adapted with a conditional masked LLM (C-MLM) objective. This adaptation allows the predictive modeling of sentence tokens to consider not only the surrounding contextual information but also the labels associated with the sentences.

Core Contributions:

1. Conditional MLM Task: The authors propose a fine-tuning method for BERT, enabling it to perform contextual word replacements that maintain compatibility with sentence labels. This model surpassed conventional augmentation methods by predicting label-compatible words, thus generating meaningful and coherent augmented sentences.
2. Experimental Validation: Across six diverse text classification tasks (e.g., SST5, SST2, Subj, MPQA, RT, TREC), the paper demonstrates that integrating conditional BERT with convolutional and recurrent neural network classifiers consistently boosts classification accuracy. Notably, the conditional BERT outperforms both the vanilla BERT model and other contextual augmentation techniques.
3. Application to Style Transfer: Beyond classification, the augmented model shows promising results in style transfer tasks, converting sentences from one style (or sentiment) to another while preserving original contextual semantics.

Numerical Results

The empirical evaluations revealed that conditional BERT contextual augmentation provided significant performance improvements over baseline models. For instance, in comparison across the SST5 dataset using CNN architectures, the proposed method achieved an accuracy of 42.3%, outperforming both context and context+label based approaches at 41.9% and 42.1%, respectively. These findings underscore the effectiveness of integrating label constraints in contextual augmentation tasks.

Implications and Future Directions

The implications of this work extend across several fronts in NLP:

• Robustness in Text Data Augmentation: By incorporating label constraints, the conditional BERT approach ensures augmented data maintains semantic fidelity, crucial for tasks requiring nuanced understanding.
• Expansion to Other Tasks: The flexibility demonstrated in style transfer tasks suggests potential adaptations of conditional augmentation in other domain-specific augmentation needs.
• Handling Imbalanced Datasets: Future work could explore applying this conditional framework to strike a more balanced representation of minority classes in imbalanced datasets, thereby enhancing model fairness and accuracy.

As we project towards broader future applications, the integration of semantic constraints within LLMs is an area ripe for exploration. Furthermore, the scalability of the conditional BERT model to handle larger text structures like paragraphs or entire documents presents a promising avenue for enhancing document-level understanding tasks.

In summary, this paper delivers a substantive advancement in augmenting labeled text data using a refined BERT framework, marking a noteworthy contribution to the field of natural language processing.