Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?
Table 2: The per-class precision, recall, and F1-score (in %) achieved by the compared models on the pre-trained GloVe embeddings with additional retrofitting using Mittens.
['[BOLD] Model', '[BOLD] Class', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1']
We compare three variants of CNN-based models with CNN and BiLSTM baselines [Zelasko2018]. Each model is evaluated using precision, recall, and F1 score for each punctuation class separately. In addition, we investigate the similarity of words when represented using the original GloVe vectors vs. vectors retrofitted using Mittens.