Punctuation prediction from conversational language
Speech transcripts generated by Automatic Speech Recognition (ASR) systems typically do not contain any punctuation or capitalization. In longer stretches of automatically recognized speech, lack of punctuation affects the general clarity of the output text. The primary purpose of punctuation restoration (PR), punctuation prediction (PP), and capitalization restoration (CR) as a distinct natural language processing (NLP) task is to improve the legibility of ASR-generated text and possibly other types of texts without punctuation. For the purposes of this task, we define PR as restoration of originally available punctuation from read speech transcripts (which was the goal of a separate task in the PolEval 2021 competition) [2] and PP as prediction of possible punctuation in transcripts of spoken/ conversational language. Aside from their intrinsic value, PR, PP, and CR may improve the performance of other NLP aspects such as Named Entity Recognition (NER), part-of-speech (POS), and semantic parsing or spoken dialog segmentation.