Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories and news. We conjecture that such issues result from representing entities as static embeddings of superficial words, while neglecting to model their ever-changing states, i.e., the information they carry, as the text unfolds. Therefore, we extend the Transformer model to dynamically conduct entity state updates and sentence realization for narrative generation. We propose a contrastive framework to learn the state representations in a discrete space, and insert additional attention layers into the decoder to better exploit these states. Experiments on two narrative datasets show that our model can generate more coherent and diverse narratives than strong baselines with the guidance of meaningful entity states.

本文提出一种基于Transformer模型的对话生成算法,在动态更新实体状态以及句子实现方面做了扩展,并引入了对比学习框架来学习状态表示,实验表明其比现有基线算法在生成连贯且多样化的文本方面具有更好的表现。

利用对比框架学习动态离散实体状态生成连贯的叙述