Improving Formality Style Transfer with Context-Aware Rule Injection.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Pre-trained language models struggle with user-generated text due to distinct linguistic styles.
• Formality style transfer (FST) aims to adapt text to a more formal or informal style.
• Existing methods often fall short when applied to diverse, real-world language data.

Purpose of the Study:

• To introduce Context-Aware Rule Injection (CARI), a novel method for formality style transfer.
• To improve the adaptability of pre-trained language models to user-generated content.
• To enhance the performance of sentiment analysis on social media data.

Main Methods:

• Developed CARI, a BERT-based encoder-decoder model incorporating context-aware rule injection.
• Implemented a mechanism for the model to dynamically select optimal rules based on input context.
• Utilized an end-to-end training approach for seamless integration of rule injection.

Main Results:

• Achieved state-of-the-art performance on the FST benchmark dataset through intrinsic evaluation.
• Demonstrated significant improvements in tweet sentiment analysis tasks via extrinsic evaluation.
• Showcased CARI's effectiveness in adapting pre-trained models to informal language styles.

Conclusions:

• CARI offers a robust solution for formality style transfer in user-generated text.
• The context-aware rule injection mechanism is key to CARI's superior performance.
• This method has practical implications for improving NLP applications dealing with social media data.