Stacked DeBERT: All attention in incomplete data for text classification.

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Current language models struggle with incomplete or noisy text data.
• Incomplete data, such as missing words or errors, significantly degrades model performance.
• Existing models are primarily trained on clean data, limiting their robustness.

Purpose of the Study:

• To introduce Stacked DeBERT, a novel model designed for robust performance on incomplete text data.
• To improve the ability of language models to extract meaningful features from noisy inputs.
• To enhance sentiment and intent classification tasks in the presence of text imperfections.

Main Methods:

• Developed a novel encoding scheme within the BERT architecture.
• Utilized denoising transformers to generate richer input representations.
• Employed multilayer perceptrons for reconstructing word embeddings and bidirectional transformers for improved representation.

Main Results:

• Achieved improved F1-scores on sentiment and intent classification tasks.
• Demonstrated enhanced robustness in handling informal text, such as tweets.
• Showcased superior performance on texts containing Speech-to-Text errors.

Conclusions:

• Stacked DeBERT offers a significant improvement in handling incomplete and noisy text data.
• The model's architecture effectively extracts features from imperfect linguistic inputs.
• This approach enhances the reliability of NLP models in real-world, data-imperfect scenarios.