Self-attention Based Model for Punctuation Prediction Using Word and Speech Embeddings

Abstract

This paper proposes to use self-attention based model to predict punctuation marks for word sequences. The model is trained using word and speech embedding features which are obtained from the pre-trained Word2Vec and Speech2Vec, respectively. Thus, the model can use any kind of textual data and speech data. Experiments are conducted on English IWSLT2011 datasets. The results show that the self-attention based model trained using word and speech embedding features outperforms the previous state-of-the-art single model by up to 7.8% absolute overall F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score. The results also show that it obtains performance improvement by up to 4.7% absolute overall F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score against the previous best ensemble model.