Neural Natural Language Inference Models Enhanced with External Knowledge
Table 2: Accuracies of models on SNLI.
['[BOLD] Model', '[BOLD] Test']
The proposed model, namely Knowledge-based Inference Model (KIM), which enriches ESIM with external knowledge, obtains an accuracy of 88.6%, the best single-model performance reported on the SNLI dataset. The difference between ESIM and KIM is statistically significant under the one-tailed paired t-test at the 99% significance level. In addition to that, we also use 15 semantic relation features, which does not bring additional gains in performance. To further investigate external knowledge, we add TransE relation embedding, and again no further improvement is observed on both the development and test sets when TransE relation embedding is used (concatenated) with the semantic relation vectors. We consider this is due to the fact that TransE embedding is not specifically sensitive to inference information; e.g., it does not model co-hyponyms features, and its potential benefit has already been covered by the semantic relation features used.