Related papers: Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework

Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework

URL: http://arxiv.org/abs/2208.03985v1
Date: Mon, 8 Aug 2022 09:02:19 GMT
Title: Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework
Authors: Jian Guan, Zhenyu Yang, Rongsheng Zhang, Zhipeng Hu, Minlie Huang
Abstract summary: We extend the Transformer model to dynamically conduct entity state updates and sentence realization for narrative generation. Experiments on two narrative datasets show that our model can generate more coherent and diverse narratives than strong baselines.
Score: 68.1678127433077
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.