Text Compression-Aided Transformer Encoding.

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Transformer encoders, utilizing self-attention, excel at general text encoding in NLP.
• However, they may not sufficiently focus on the core gist or backbone information of the input text.
• This limitation can impact performance on downstream tasks heavily reliant on concise text representations.

Purpose of the Study:

• To propose and evaluate explicit and implicit text compression techniques to enhance Transformer-based text encoding.
• To investigate methods for integrating compressed backbone information into Transformer models.
• To improve the learning of language representations for better performance on downstream NLP tasks.

Main Methods:

• Developed explicit text compression models using dedicated architectures.
• Introduced an implicit text compression module integrated into the main Transformer model.
• Proposed three fusion strategies: backbone source-side, target-side, and both-side fusion.
• Evaluated the enhanced models on several benchmark downstream NLP tasks.

Main Results:

• Both explicit and implicit text compression approaches demonstrated performance improvements over strong baselines.
• The proposed fusion methods effectively integrated backbone information into Transformer models.
• Text compression was shown to aid encoders in learning superior language representations.

Conclusions:

• Text compression is a valuable technique for enhancing Transformer encoders in Natural Language Processing.
• Explicit and implicit compression methods, along with strategic fusion, lead to improved text representations.
• The findings suggest that focusing on text compression can significantly benefit various NLP applications.