Improving Natural Language Inference with a Pretrained Parser
Table 1: NLI dev./test accuracies. Bold is the top value on one dataset/architecture pair. Blue numbers represent accuracy increases and red numbers decreases, both relative to the syntax-free baseline of the same architecture.
['Model', 'SNLI dev.', 'SNLI test', 'SciTail dev.', 'SciTail test', 'MNLI-m dev.', 'MNLI-m test', 'MNLI-mm dev.', 'MNLI-mm test']
We find that adding representations from the syntax parser (+LF, +SA) always improves test accuracy on SNLI and SciTail across all baseline models. On MNLI, the results are mixed—DA, BERT, and MT-DNN improve on the matched subset, and only DA and MT-DNN improve on the mismatched subset.