Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end
deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is 
the large number of out-of-vocabulary words. In this paper we proposed an algorithm which combines the general pre-trained word embedding vectors with those  generated on the task-specific training set to address this issue.   We integrated character embedding into Chen et al's Enhanced LSTM method (ESIM) and used it to evaluate the effectiveness of our proposed method. For the task of next utterance selection, the proposed method has demonstrated a significant performance improvement against original ESIM and the new model has achieved state-of-the-art results on both Ubuntu dialogue corpus and Douban conversation corpus. In addition, we investigated the performance impact of end-of-utterance and end-of-turn token tags.
Combine information between pre-built word embedding and task-specific word representation to address out-of-vocabulary issue This paper proposes an approach to improve the out-of-vocabulary embedding prediction for the task of modeling dialogue conversations with sizable gains over the baselines. Proposes combining external pretrained word embeddings and pretrained word embeddings on training data by keeping them as two views. Proposes method to extend the coverage of pre-trained word embeddings to deal with the OOV problem that arises when applying them to conversational datasets and applies new variants of LSTM-based model to the task of response-selection in dialogue modeling.