Zero-shot User Intent Detection via Capsule Neural Networks
Table 1: Intention detection results using IntentCapsNet on two datasets. All the metrics (Accuray, Precision, Recall and F1) are reported using the average value weighted by their support on per class.
['[BOLD] Model', '[BOLD] SNIPS-NLU (on 5 existing intents) Accuracy', '[BOLD] SNIPS-NLU (on 5 existing intents) Precision', '[BOLD] SNIPS-NLU (on 5 existing intents) Recall', '[BOLD] SNIPS-NLU (on 5 existing intents) F1', '[BOLD] CVA (on 80 existing intents) Accuracy', '[BOLD] CVA (on 80 existing intents) Precision', '[BOLD] CVA (on 80 existing intents) Recall', '[BOLD] CVA (on 80 existing intents) F1']
Quantitative Evaluation. These results demonstrate the novelty and effectiveness of the proposed capsule-based model IntentCapsNet in modeling text for intent detection.