A large annotated corpus for learning natural language inference
Table 5: 3-class accuracy, training on either our data or SICK, including models lacking cross-bigram features (Feature 6), and lacking all lexical features (Features 4–6). We report results both on the test set and the training set to judge overfitting.
['[BOLD] System', '[BOLD] SNLI Train', '[BOLD] SNLI Test', '[BOLD] SICK Train', '[BOLD] SICK Test']
On our large corpus in particular, there is a substantial jump in accuracy from using lexicalized features, and another from using the very sparse cross-bigram features. The latter result suggests that there is value in letting the classifier automatically learn to recognize structures like explicit negations and adjective modification. A similar result was shown in \newcitesidaw12simple for bigram features in sentiment analysis.