Punctuation Mark

Punctuation restoration and prediction are active research areas focusing on automatically adding or correcting punctuation marks in text, primarily from speech recognition outputs or other sources lacking proper formatting. Current research employs various deep learning models, including transformers, convolutional neural networks, and recurrent neural networks, often incorporating techniques like attention mechanisms and connectionist temporal classification to improve accuracy and efficiency, particularly for real-time applications. This work is crucial for improving the readability and usability of automatically generated text, impacting downstream NLP tasks and applications such as machine translation and Alzheimer's disease diagnosis through speech analysis.