Measuring the coherence of textual content is a significant facet of evaluating the standard of composed material. Recent enhancements in neural coherence modeling have demonstrated their efficacy in capturing entity coreference and discourse relations, therefore enhancing coherence evaluation.
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However, lots of present approaches greatly rely on static embeddings or concentrate narrowly on close by context, constraining their ability to measure the overarching coherence of long texts.
On the other hand, these strategies yield static representations that overlook the dynamics of the whole text. In a recent study, Wang et al. (2023) proposed an innovative method employing the Brownian bridge to product very long-array Live Football TV text dependencies. The proposed approach exhibited outstanding efficiency in producing long and coherent discourses compared to the baseline GPT2 product. With this paper, we lengthen their findings by introducing a chance measurement and conducting experiments to assess its efficacy in capturing each global textual content coherence and local coherence.
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See PDF HTML (experimental) Summary:Measuring the coherence of textual content is a vital facet of analyzing the quality of published content. Modern advancements in neural coherence modeling have demonstrated their efficacy in Live Football TV capturing entity coreference and discourse relations, therefore maximizing coherence evaluation. Nonetheless, lots of existing strategies intensely depend on static embeddings or target narrowly on close by context, constraining their potential to evaluate the overarching coherence of lengthy texts. In this particular paper, we posit that coherent texts inherently manifest a sequential and cohesive interaction among the sentences, successfully conveying the central theme, function, or standpoint. To examine this summary relationship, we introduce the "BBScore," a novel reference-free of charge metric grounded in Brownian bridge principle for evaluating textual content coherence.
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