Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference
Table 4: Performance on the SNLI dataset and the MultiNLI dataset. In the top part, we show sentence encoding-based models; In the medium part, we present the performance of integrated neural network models; In the bottom part, we show the results of ensemble models.
['[BOLD] Method', '[BOLD] SNLI', '[BOLD] MultiNLI  [BOLD] Matched', '[BOLD] MultiNLI  [BOLD] Mismatched']
As we can see, our method Discourse Marker Augmented Network (DMAN) clearly outperforms all the baselines and achieves the state-of-the-art results on both datasets. We build an ensemble model which consists of 10 single models with the same architecture but initialized with different parameters. The performance of our model achieves 89.6% on SNLI, 80.3% on matched MultiNLI and 79.4% on mismatched MultiNLI, which are all state-of-the-art results.