A Compare-Aggregate Model with Latent Clustering for Answer Selection
Table 3. Model (Comp-Clip +LM +LC) performance on the QNLI corpus with a variant number of clusters (top score marked as bold).
['[BOLD] # Clusters', '[BOLD] Listwise Learning MAP', '[BOLD] Listwise Learning MRR', '[BOLD] Pointwise Learning MAP', '[BOLD] Pointwise Learning MRR']
To evaluate the impact of latent clustering method (Comp-Clip +LM +LC) in a larger dataset environment, we perform QNLI evaluation. Note that the QNLI dataset is created from the SQuAD (rajpurkar2016squad) dataset, which only provides train and dev subsets. Consequently, we report the model performances for the dev dataset. As shown in the table, we achieve the best results with 8 clusters in listwise learning and 16 clusters in pointwise learning. In both cases, we achieve no additional performance gain after 16 clusters.