Supervised Learning of Universal Sentence Representations fromNatural Language Inference Data
Table 3: Performance of sentence encoder architectures on SNLI and (aggregated) transfer tasks. Dimensions of embeddings were selected according to best aggregated scores (see Figure 5).
['[BOLD] Model', '[BOLD] dim', '[BOLD] NLI  [BOLD] dev', '[BOLD] NLI  [BOLD] test', '[BOLD] Transfer  [BOLD] micro', '[BOLD] Transfer  [BOLD] macro']
S5SS1SSS0Px1 Model The BiLSTM-4096 with the max-pooling operation performs best on both SNLI and transfer tasks. Looking at the micro and macro averages, we see that it performs significantly better than the other models LSTM, GRU, BiGRU-last, BiLSTM-Mean, inner-attention and the hierarchical-ConvNet.