Punctuation Restoration

Punctuation restoration aims to automatically add missing punctuation marks to text, primarily from unpunctuated transcripts generated by automatic speech recognition (ASR) systems. Current research focuses on leveraging deep learning models, particularly transformer-based architectures like BERT and LLMs, often incorporating multimodal approaches that fuse textual and acoustic information for improved accuracy. This field is crucial for enhancing the readability and usability of ASR outputs, improving downstream NLP tasks, and facilitating analysis of historical texts lacking punctuation. The development of efficient and accurate models is driving progress in various applications, including transcription services, text processing pipelines, and historical document analysis.