Before revision:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
After revision:  After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect deep matching features for scoring response candidates . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy .