Modular Representation Underlies Systematic Generalization in Neural Natural Language Inference Models
Table 1: Adversarial testing results of our three models trained on SNLI. The numbers are accuracy values; all the datasets have balanced label distributions.
['[EMPTY]', 'SNLI', 'PMoNLI', 'NMoNLI']
The three models achieve high accuracy on SNLI and PMoNLI, the examples where no downward monotone operators scope over the substitution site. However, they are well below chance accuracy on NMoNLI, the examples where not scopes over the substitution site. BERT is more extreme than the other models, achieving a higher accuracy on PMoNLI than SNLI and almost zero accuracy on NMoNLI. High performance on PMoNLI shows that models have knowledge of the lexical relations between the substituted words, but low performance on NMoNLI shows the models have no knowledge of the downward monotone nature of not. In fact, the below chance accuracy on NMoNLI indicates that these models are somewhat reliably (incredibly reliably in BERT’s case) predicting the wrong label on these examples, suggesting that they might be treating these examples the same as they do examples from PMoNLI.