Uncertain Natural Language Inference
Table 5: Metrics for training only on mapped SNLI or fine-tuning on u-SNLI.
['[EMPTY]', '[BOLD] SNLI  [BOLD] Dev', '[BOLD] SNLI  [BOLD] Test', '[BOLD] SNLI +  [ITALIC] u [BOLD] -SNLI  [BOLD] Dev', '[BOLD] SNLI +  [ITALIC] u [BOLD] -SNLI  [BOLD] Test']
We use this mapping to pre-train a regression model on the SNLI training examples not included in u-SNLI. We also fine-tune the model on u-SNLI’s training set. The model trained on the roughly 500K mapped SNLI examples, performs much worse than when trained on just about 55K u-SNLI examples. When we pre-train the model on the mapped SNLI and fine-tune on u-SNLI, results noticeably improve. This improvement is akin to the \newcitestilt’s finding that many NLI datasets cover informative signal for different tasks, explaining why pre-training on NLI can be advantageous. Here, an impoverished version of UNLI is helpful.