Compound Conversation Model Combining Retrieval and Generation

Abstract

Conversation model is one of the important directions of natural language processing.Today's dialogue models are mainly divided into retrieval-based methods and generation-based methods.However,the retrieval method cannot respond to questions that do not appear in the corpus,and the generation method is prone to problems with safe responses.In view of this,a compound conversation model that combines retrieval and generation is proposed,and the retrieval method and generation method are combined to make up for their shortcomings.First,K retrieval contexts and corresponding K retrieval candidate responses are obtained through the retrieval module.In the multi-response generation module,retrieval contexts are further combined to obtain several generation candidate responses.The candidate response ranking module is divided into two steps:pre-screening and post-reranking.The pre-screening part obtains the optimal retrieval response and the optimal generated response by calculating the similarity between the input question and candidate responses,and the post-reranking part further selects the most suitable answer to the input question.Experimental results show that the BLUE index increased by 6%,and the diversity index increased by 12%.

Keywords

• conversation system|retrieval model|generation model|transformer|post-reranking