论文标题
分析神经话语相干模型
Analyzing Neural Discourse Coherence Models
论文作者
论文摘要
在这项工作中，我们系统地研究了当前的连贯性模型如何捕获与话语组织有关的文本方面。我们设计了两个语言变化的两个数据集，这些数据集破坏了对语法和语义变化的一致性和测试模型敏感性。我们此外，我们的探针话语嵌入了空间，并研究了在连贯性表示中编码的知识。我们希望这项研究应提供有关如何制定任务并进一步改善连贯评估模型的进一步见解。最后，我们将数据集公开用作研究人员用于测试话语连贯模型的资源。
In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various linguistic alterations that undermine coherence and test model sensitivity to changes in syntax and semantics. We furthermore probe discourse embedding space and examine the knowledge that is encoded in representations of coherence. We hope this study shall provide further insight into how to frame the task and improve models of coherence assessment further. Finally, we make our datasets publicly available as a resource for researchers to use to test discourse coherence models.
