Conversation generation is an important natural language processing task and has attracted much attention in recent years. The realization of the conversation model is also of great significance to the field of social computing, helping to build artificial intelligence robots on social networks. The open domain conversation model is fundamentally data-driven, which can be roughly divided into retrieval models and generation models. Although remarkable progress has been achieved in recent years, it is still difficult to get responses that are grammatically and semantically appropriate. We propose the Rerank of Retrieval-based and Transformer-based Conversation model (RRT), a novel conversation model that combines the retrieval model with the generation model for the purpose of obtaining context–appropriate response. The context–response pairs with the highest similarity from training set are retrieved using traditional retrieval method, and further ranked to obtain the retrieval candidate response. We replaced the traditional sequence-to-sequence models for conversation generation by the transformer model and achieved better results with less training time. Finally, the post-reranking module is used to rank the retrieved candidate and the generated one to obtain the final response. We conducted detailed experiments on two datasets and the results show that compared with the traditional generation model, our model has a significant improvement in each metric, and the training time is decreased by a factor of 5. Furthermore, our model is more informative and relevant to the input context than the retrieval model.

会话生成是一项重要的自然语言处理任务，近年来引起了很多关注。对话模型的实现对社交计算领域也具有重要意义，有助于在社交网络上构建人工智能机器人。开放域对话模型从根本上讲是数据驱动的，可以大致分为检索模型和生成模型。尽管近年来已经取得了显着的进步，但是仍然很难获得语法和语义上适当的答案。我们提出了基于检索和基于变压器的对话模型（RRT），这是一种新颖的对话模型，该模型将检索模型与生成模型相结合，目的是获得上下文适当的响应。使用传统的检索方法来检索训练集中具有最高相似性的上下文响应对，并对其进行进一步排名以获得检索候选响应。我们用转换器模型代替了传统的序列到序列模型来进行对话生成，并以更少的训练时间获得了更好的结果。最后，后重排名模块用于对检索到的候选者和生成的候选者进行排名，以获得最终响应。我们对两个数据集进行了详细的实验，结果表明，与传统的生成模型相比，我们的模型在每个指标上都有显着改进，并且训练时间减少了5倍。此外，我们的模型更具信息性和相关性输入上下文而不是检索模型。