Semantic Sentence Matching with Densely-connectedRecurrent and Co-attentive Information
Table 3: Classification accuracy for natural language inference on MultiNLI test set. * denotes ensemble methods.
['[BOLD] Models', '[BOLD] Accuracy (%)  [BOLD] matched', '[BOLD] Accuracy (%)  [BOLD] mismatched']
Our plain DRCN has a competitive performance without any contextualized knowledge. And, by combining DRCN with the ELMo, one of the contextualized embeddings from language models, our model outperforms the LM-Transformer which has 85m parameters with fewer parameters of 61m. From this point of view, the combination of our model with a contextualized knowledge is a good option to enhance the performance.