Measuring the coherence of text is a vital element of assessing the standard of published information. Latest progress in neural coherence modeling have demonstrated their efficacy in capturing entity coreference and discourse relations, thereby boosting coherence evaluation.
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As EuroCup 2024 techniques, the enjoyment is creating among the fans and analysts alike, who're eager to determine which groups will arise […]
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Measuring the coherence of textual content is a vital facet of analyzing the quality of prepared content. Modern advancements in neural coherence modeling have demonstrated their efficacy in capturing entity coreference and discourse relations, thus enhancing coherence evaluation.
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Nevertheless, these ways produce static representations that overlook the dynamics of your complete textual content. Within a current study, Wang et al. (2023) proposed an ground breaking strategy using the Brownian bridge to product very long-assortment textual content dependencies. The proposed system exhibited excellent overall performance in building prolonged and coherent discourses in comparison with the baseline GPT2 product. In this particular paper, we extend their conclusions by introducing a likelihood measurement and conducting experiments to assess its efficacy in capturing both of those world-wide text coherence and native coherence.
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Perspective PDF HTML (experimental) Abstract:Measuring the coherence of textual content is a significant aspect of assessing the standard of composed content. Modern advancements in neural coherence modeling have demonstrated their efficacy in capturing entity coreference and discourse relations, thus enhancing coherence evaluation. On the other hand, numerous current solutions intensely depend upon static embeddings or aim narrowly on nearby context, constraining their capability to evaluate the overarching coherence of extensive texts. On this paper, we posit that coherent texts inherently manifest a sequential and cohesive interaction among the sentences, successfully conveying the central concept, reason, or standpoint. To explore this abstract connection, we introduce the "BBScore," a novel reference-no cost metric grounded in Brownian bridge theory for assessing text coherence.
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