Measuring the coherence of textual content is an important element of evaluating the standard of created information. Current progress in neural coherence modeling have demonstrated their efficacy in capturing entity coreference and discourse relations, therefore maximizing coherence evaluation.
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Even so, these strategies yield static representations that forget the dynamics of the entire text. In the new analyze, Wang et al. (2023) proposed an innovative approach making use of the Brownian bridge to model extensive-range textual content dependencies. The proposed process exhibited outstanding effectiveness in building long and coherent discourses when compared to the baseline GPT2 product. In this particular paper, we extend their findings by introducing a chance measurement and conducting experiments to evaluate its efficacy in capturing both equally world wide textual content coherence and local coherence.
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It encompasses the stream and connectivity of ideas, the reasonable development of knowledge, and The graceful transition amongst sentences and paragraphs.
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