[
Table 8: MultiNLI-matched confusion matrices for HBMP and InferSent based on the development set.
['[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 matched dataset (MultiNLI-m) our model had a development accuracy of 74.1%. For MultiNLI-m our model has the best accuracy on sentence pairs labeled with entailment, having an F-score of 77.2%. The model is also almost as accurate in predicting contradictions, with an F-score of 75.3%. Similar to SNLI, our model is less effective on sentence pairs labeled with neutral, having an F-score of 68.2% but, again, the HBMP model outperforms the InferSent on all the labels. Our model improves upon InferSent in all values of precision and recall, in some cases by a wide margin.