A Hybrid Deep Learning Architecture for Sentence Unit Detection

Automatic speech recognition systems currently deliver an unpunctuated sequence of words which is hard to peruse for human and degrades the performance of the downstream natural language processing tasks. In this paper, we propose a hybrid approach for Sentence Unit Detection, in which the focus is on adding the full stop [.]to the unstructured text. Our model profits from the advantage of two dominant deep learning architectures: (i)the ability to learn the long dependencies in both directions of a bidirectional Long Short-Term Memory; (ii)the ability to capture the local context with Convolutional Neural Networks. We also empirically study the training objective of our networks using extra-loss and further investigate the impacts of each model component on the overall result. Experiments conducted on two large-scale datasets demonstrated that the proposed architecture outperforms previous separated methods by a substantial margin of 1.82-1.91% of F₁. Availability: the source code and model are available at https://github.com/catcd/LSTM-CNN-SUD.