DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
Table 3: Experimental results of prediction accuracy for different methods on MultiNLI.
['[BOLD] Method', '[BOLD] Matched', '[BOLD] Mismatched']
This dataset is similar to SNLI, but it covers more genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. However, MultiNLI is a quite new dataset, and its leaderboard does not include a session for the sentence-encoding only model. Hence, we only compare DiSAN with the baselines provided at the official website. MultiNLI Matched Open Evaluation and MultiNLI Mismatched Open Evaluation.