On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression
Table 3: The table shows the Accuracy/F1 with ELMo.
['[BOLD] SNLI', '[BOLD] r=10  [BOLD] Acc.', '[BOLD] r=10  [BOLD] E(r)', '[BOLD] r=400  [BOLD] Acc.', '[BOLD] r=400  [BOLD] E(r)', '[BOLD] Best  [BOLD] Acc. (avg)', '[BOLD] Best  [BOLD] E(r) (avg)']
In fact, we can see that, in average, using highly compressed ELMo with BiDAF still performs better than without. Overall, we can see that for all datasets, we achieve performances that are not significantly different from the baseline results even after compressing over more than 10M parameters.