Simple and Effective Text Matching with Richer Alignment Features
Table 6: Ablation study on dev sets of the corresponding datasets.
['[EMPTY]', '[BOLD] SNLI', '[BOLD] Quora', '[BOLD] Scitail', '[BOLD] WikiQA']
The first ablation baseline shows that without richer features as the alignment input, the performance on all datasets degrades significantly. This is the key component in the whole model. The results of the second baseline show that vanilla residual connections without direct access to the original point-wise features are not enough to model the relations in many text matching tasks. The simpler implementation of the fusion layer leads to evidently worse performance, indicating that the fusion layer cannot be further simplified. On the other hand, the alignment layer and the prediction layer can be simplified on some of the datasets. In the last ablation study, we can see that parallel blocks perform worse than stacked blocks, which supports the preference for deeper models over wider ones.