论文标题
通过上下文表示，改善话语依赖性解析
Improve Discourse Dependency Parsing with Contextualized Representations
论文作者
论文摘要
最近的作品表明，话语分析得益于分别对内部和索引间级别进行建模，其中需要适当的不同粒度单位的文本单位表示，以捕获文本单元的含义及其与上下文的关系。在本文中，我们建议利用变形金刚编码不同级别单位的上下文化表示，以动态捕获对话语依赖性分析对内部和句子间级别所需的信息。我们提出了一种新的方法，该方法将话语关系识别视为序列标记任务，从而利用了从提取的话语树的上下文中利用结构信息，并且实质上优于传统的直接分类方法，因此我们提出了一种新颖的方法，该方法将话语关系识别视为序列标记任务。实验表明，我们的模型可以在英语和中文数据集上获得最新的结果。
Recent works show that discourse analysis benefits from modeling intra- and inter-sentential levels separately, where proper representations for text units of different granularities are desired to capture both the meaning of text units and their relations to the context. In this paper, we propose to take advantage of transformers to encode contextualized representations of units of different levels to dynamically capture the information required for discourse dependency analysis on intra- and inter-sentential levels. Motivated by the observation of writing patterns commonly shared across articles, we propose a novel method that treats discourse relation identification as a sequence labelling task, which takes advantage of structural information from the context of extracted discourse trees, and substantially outperforms traditional direct-classification methods. Experiments show that our model achieves state-of-the-art results on both English and Chinese datasets.
