The difference to that work is that here we use language-specific BERT models for each featured language. We had recently demonstrated, that such an approach achieves state-of-the-art performance when identifying end-of-sentence markers on automatically transcribed texts. Our submissions are based on pre-trained BERT models that have been fine-tuned to the task at hand. We participated in Subtask 1 (fully un-punctuated sentences-full stop detection) and submitted a run for every featured language (English, German, French, Italian). This paper describes our approach (UR-mSBD) to address the shared task on Sentence End and Punctuation Prediction in NLG Text (SEPP-NLG) organised as part of SwissText 2021. In addition, we expect that language barriers in online education will be more easily broken by achieving more accurate translations of numerous video lectures in English. Our research will provide people with more accurate translations of subtitles. We build a model with the training data using the LSTM-RNN (Long-Short Term Memory – Recurrent Neural Networks) and predict the position of the period mark, resulting in prediction accuracy of 70.84%. Since this lecture video provides complete sentence caption data, it can be used as training data by transforming the subtitles into general YouTube-like caption data. For this study, we use the 27,826 sentence subtitles provided by Stanford University’s courses as data. In this paper, we propose a method to divide text into sentences and generate period marks to improve the accuracy of automatic translation of English subtitles. Since the generated subtitles are separated by time units rather than sentence units, and are translated, it is very difficult to understand the translation result as a whole. However, when extracting subtitles from video using Speech to Text, it is impossible to accurately translate the sentence because all sentences are generated without periods.

This method creates subtitles suitable for the running time.

Currently, the automatic caption system extracts voice data when uploading a video and provides a subtitle file converted into text. Using this, YouTube, a video-sharing site, provides captions in many languages.

Recently, with the development of Speech to Text, which converts voice to text, and machine translation, technologies for simultaneously translating the captions of video into other languages have been developed.

Youtube subtitles into text