论文标题
神经首先对话语连贯性评估
Neural RST-based Evaluation of Discourse Coherence
论文作者
论文摘要
本文评估了修辞结构理论（RST）树木的实用性以及话语连贯评估中的关系。我们表明，在对连贯性分类时，合并银标准的RST功能可以提高准确性。我们通过我们的树木恢复神经模型，即第一回收，它利用了文本的第一个特征，该特征是由最先进的rst解析器产生的。我们评估了语法语料库（GCDC）的方法，并表明当与当前的最新水平状态时，我们可以在此基准上实现新的最新准确性。此外，当单独部署时，RST Recursive可以达到竞争精度，而参数少62％。
This paper evaluates the utility of Rhetorical Structure Theory (RST) trees and relations in discourse coherence evaluation. We show that incorporating silver-standard RST features can increase accuracy when classifying coherence. We demonstrate this through our tree-recursive neural model, namely RST-Recursive, which takes advantage of the text's RST features produced by a state of the art RST parser. We evaluate our approach on the Grammarly Corpus for Discourse Coherence (GCDC) and show that when ensembled with the current state of the art, we can achieve the new state of the art accuracy on this benchmark. Furthermore, when deployed alone, RST-Recursive achieves competitive accuracy while having 62% fewer parameters.
