Semantic Sentence Matching with Densely-connectedRecurrent and Co-attentive Information
Table 6: Accuracy (%) of Linguistic correctness on MultiNLI dev sets.
['[BOLD] Category', '[BOLD] ESIM', '[BOLD] DIIN', '[BOLD] CAFE', '[BOLD] DRCN']
We used annotated subset provided by the MultiNLI dataset, and each sample belongs to one of the 13 linguistic categories. Especially, we can see that ours outperforms much better on the Quantity/Time category which is one of the most difficult problems. Furthermore, our DRCN shows the highest mean and the lowest stddev for both matched and mismatched problems, which indicates that it not only results in a competitive performance but also has a consistent performance.