基于外部知识筛选的主题文本生成技术研究OA北大核心CSTPCD

In the natural language generation task,topic text generation is a challenging task,the main difficulty is that the amount of source information is much smaller than the amount of information gener-ated by the target.To solve this problem,this paper proposes a topic text generation model called Trans-K based on external knowledge filtering,which enriches the source information by introducing ex-ternal knowledge related to topic words,thereby improving the quality of the generated text.In this pa-per,in order to solve the"polysemy"problem of introducing external knowledge,a topic vector calcula-tion method based on linear transformation is proposed to filter external knowledge consistent with the semantics of the topic words.An external weight calculation method based on attention mechanism is proposed,which sets a topic weight for each external word to make it more suitable for text semantics.In order to solve the problem that topic words including candidate words,appear repeatedly in the gener-ated text,an internal weight calculation method based on the multi-head attention mechanism is pro-posed.Experiments on the EASSY dataset show that Trans-K is superior to various indicators of the quality of generated text compared to the baseline.In addition,human evaluations show that the model can generate more topic-relevant,linguistically coherent,and semantically logical's text.