FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
Table 10: The performance (accuracy) of ESIM with our proposed attention enhancement on MultiNLI (Williams et al., 2017) development set. (d is the output hidden size of BiLSTM)
['[EMPTY]', '[BOLD] Cross-Domain', '[BOLD] In-Domain']
Augmenting with fully-aware attention yields the biggest improvement, which demonstrates the usefulness of this simple enhancement. Further improvement is obtained when we use multi-level fusion in our ESIM. Experiments with and without CoVe embedding show similar observations.