一种端到端的生成式问答模型

he question and answer system is one of the important research directions in the field of natural language processing, and has received more and more attention in recent years. Based on the two-way attention mechanism and the "copy-generation" mechanism, this paper proposes a novel end-to-end generated question-and-answer model, which integrates the answer extraction and generation processes, and introduces the Coverage mechanism to alleviate the problem of generating duplicates. Compared with the classic question-and-answer model, the end-to-end fusion model can extract key information such as paragraph entities and unregistered words on the one hand, and generate answers in natural language form on the other hand. Experiments were carried out on the MS MARCO v2.1 dataset. The end-to-end fusion model is superior to the purely generated model and the purely extracted model in terms of Rouge-L and BLEU-n.