基于拓展关键词信息的对话生成模型

在对话过程中,人们通常根据对方上一句话的关键词做出相应的回复。为了生成与关键词含义相关的回复,提出了拓展关键词信息注意力机制的对话生成模型。首先从输入语句中提取关键词,然后根据关键词词向量余弦相似度找出与关键词相关的词语构成拓展关键词集合,将集合中词语的词向量通过注意力机制的方式加入解码过程来影响回复生成。在中文微博数据集及英文Twitter数据集上的实验表明,该模型在回复语句的相关性及多样性方面取得了优于其他模型的结果。

Dialogue generation model based on extended keywords information

Noting that people often reply to others based on the keywords in the post. In order to generate meaningful responses about the keywords, we proposed a dialogue generation model based on an attention mechanism with extended keywords information. First, we extract the keywords of the input, and then form the extended keywords set based on the words which are related to the input keywords using the cosine similarity. The words in the extended keywords set affect the response generation with the attention mechanism. Experiments on the Chinese Weibo dataset and the English Twitter dataset show that our model outperforms the traditional seq2seq model and its variations in both the metric-based evaluation and the human-evaluation.