Natural Language Inference over Interaction Space
Table 2: MultiNLI result.
['[BOLD] Model', '[BOLD] Test Accuracy  [BOLD] Matched', '[BOLD] Test Accuracy  [BOLD] Mismatched']
Besides ESIM, the state-of-the-art model on SNLI, all other models appear at RepEval 2017 workshop. RepEval 2017 workshop requires all submitted model to be sentence encoding-based model therefore alignment between sentences and memory module are not eligible for competition. All models except ours share one common feature that they use LSTM as a essential building block as encoder. Our approach, without using any recurrent structure, achieves the new state-of-the-art performance of 80.0%, exceeding current state-of-the-art performance by more than 5%. Unlike the observation from Nangia et al. Selecting parameters from the best in-domain development accuracy partially contributes to this result.