[
Table 9: MultiNLI-mismatched 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']
For the MultiNLI mismatched dataset (MultiNLI-mm) our model had a development accuracy of 73.7%. or MultiNLI-mm our model has very similar performance as with the MultiNLI-m dataset, having the best accuracy on sentence pars labeled with entailment, having an F-score of 77.9%. The model is also almost as accurate in predicting contradictions, with an F-score of 75.6%. Our model is less effective on sentence pairs labeled with neutral, having an F-score of 68.6%. Mismatched dataset comparing the HBMP to InferSent and the picture is similar to the result of the matched dataset. Substantial improvements can be seen again, in particular in the precision of contradiction detection.