Results Table 4 shows the results of our experiment. BiL-STM is good at capturing the sequence information of sentences; however, since it only finds some kind of answer distributions on the sequence information, it cannot capture the information of the relation between query and utterance. Adding a CNN can achieve even lower accuracy because passing sequences to the CNN only keeps important information after the pooling operation, but for dialogue data, most of the time the replaced entity needs to be decided by <cite>Ma, Jurczyk, and Choi [2018]</cite> are not helpful for these tasks on our data split because dialogues contain so many informal expressions and the size of the corpus is small.