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Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework. (arXiv:2208.03985v2 [cs.CL] UPDATED)
Nov. 24, 2022, 7:18 a.m. | Jian Guan, Zhenyu Yang, Rongsheng Zhang, Zhipeng Hu, Minlie Huang
cs.CL updates on arXiv.org arxiv.org
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 …
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