Improving Generalization by Incorporating Coverage in Natural Language Inference
Table 2: Impact of using coverage for improving generalization across different datasets of the same task (NLI). All models are trained on MultiNLI.
['[EMPTY]', 'in-domain MultiNLI', 'out-of-domain SNLI', 'out-of-domain Glockner', 'out-of-domain SICK']
Table 2 shows the performance for both systems for in-domain (the MultiNLI development set) as well as out-of-domain evaluations on SNLI, Glockner, and SICK datasets. [CONTINUE] The results show that coverage information considerably improves the generalization of both examined models across various NLI datasets. The resulting cross-dataset improvements on the SNLI and Glockner datasets are larger than those on the SICK dataset.