[
Table 6: SNLI confusion matrices for HBMP and InferSent.
['[EMPTY]', '[EMPTY]', '[BOLD] Predicted - HBMP entail', '[BOLD] Predicted - HBMP contradict', '[BOLD] Predicted - HBMP neutral', '[ITALIC] recall', '[BOLD] Predicted - InferSent entail', '[BOLD] Predicted - InferSent contradict', '[BOLD] Predicted - InferSent neutral', '[ITALIC] recall']
On the SNLI dataset our model clearly outperforms InferSent on all labels in terms of precision and recall. The precision on contradiction exceeds 90% for our model and reaches high recall values for both, entailment and contradiction. The performance is lower for neutral and the confusion of that label with both, contradiction and entailment is higher. However, HBMP still outperforms InferSent by a similar margin as for the other two labels.