SELF-ATTENTION BASED MODEL FOR PUNCTUATION PREDICTION USING WORD AND SPEECH EMBEDDINGS

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-trainedWord2Vec 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-1-score. The results also show that it obtains performance improvement by up to 4.7% absolute overall F-1-score against the previous best ensemble model.