Analyzing representations
Table 13: Biased exposure: Results from InferSent embeddings augmented with different training sets that manipulate the co-occurrence of context and token.
['Test set', 'Performance (%) Zero-shot', 'Performance (%) Exposed– [ITALIC] C1 [ITALIC] T∗', 'Performance (%) Exposed– [ITALIC] C2 [ITALIC] T∗']
All three models received the same number of training examples, with equal numbers of sentence pairs from both contexts C1 and C2. They all also saw T∗ noun phrases appear in both contexts. The three models only differed in which contexts T∗ noun phrases appeared during training. The control model never saw T∗ noun phrases, Exposed–C1T∗ only saw them in the C1 context and Exposed–C2T∗ only saw them in the C2 context. All of these were then tested on the same held-out test set. This is consistent with our initial results on augmentation (see section ‘Augmenting the learning environment’). Further, the control (zero-shot reasoning) condition that never saw T∗ noun phrases in training generalizes well to all the test examples with T∗ noun phrases (first column). This is consistent with our results on zero-shot generalization (see section ‘Zero-shot reasoning’). We discuss the results for the model Exposed–C1T∗ (that saw T∗ noun phrases in C1 type comparisons), a symmetric discussion applies also to Exposed–C2T∗. W found that Exposed–C1T∗ performs well on held-out test examples from the C1T∗ category (99.7 %) – as consistent with our original experiments with augmentation. However, we found that it fails to generalize very well to T∗ type noun phrases in the C2 context, with a significant drop in performance (67.71 %). The crucial comparison is that this low performance is also significantly worse that that of the zero-shot control on the same test set (95.78 %). Neither of these have seen T∗ phrases in the C2 context – yet the control generalizes very well, while the Exposed–C1T∗ fails to. This indicates that while the representations learned can generalize well to previously unseen tokens, this generalization is poorer to tokens that have in fact been seen before, but only in a different context.