论文标题
释放神经话语解析器的力量 - 使用大规模预审查的环境和结构意识的方法
Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining
论文作者
论文摘要
首先基于R的话语解析是一项重要的NLP任务，具有许多下游应用程序，例如摘要，机器翻译和意见挖掘。在本文中，我们演示了一个简单而又高度准确的话语解析器，结合了最近的上下文语言模型。我们的解析器建立了新的最先进的（SOTA）性能，以预测两个关键RST数据集的结构和核能，即RST-DT和INSTER-DT。我们进一步证明，在最近可用的大规模“银色标准”话语Treebank Mega-DT上进行解析器提供了更大的绩效优势，这表明了话语分析领域的新颖而有希望的研究方向。
RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining. In this paper, we demonstrate a simple, yet highly accurate discourse parser, incorporating recent contextual language models. Our parser establishes the new state-of-the-art (SOTA) performance for predicting structure and nuclearity on two key RST datasets, RST-DT and Instr-DT. We further demonstrate that pretraining our parser on the recently available large-scale "silver-standard" discourse treebank MEGA-DT provides even larger performance benefits, suggesting a novel and promising research direction in the field of discourse analysis.
