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 a method 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.

该论文提出了一种方法，将通用的预训练词向量与特定任务的训练集生成的词向量相结合，解决Ubuntu对话语料库中的大量生僻词问题，应用于下一个话语选择任务中，结果表明该方法优于原始方法ESIM，并在Ubuntu和Douban对话语料库上取得了最先进的结果。

增强Ubuntu对话语料库中未登录词的单词表示