论文标题
通过梯度提升来朝着独立于领域的监督话语解析
Towards Domain-Independent Supervised Discourse Parsing Through Gradient Boosting
论文作者
论文摘要
话语分析和话语解析对自然语言处理（NLP）领域的许多重要问题的影响很大。鉴于话语注释对模型性能和解释性的直接影响，从任意文档中鲁棒提取话语结构是进一步改善NLP中计算模型的关键任务。为此，我们提出了一个新的，有监督的范式，直接解决了话语解析中的领域适应问题。具体而言，我们介绍了第一个完全监督的话语解析器，旨在通过引入梯度增强框架，通过弱分类器的分阶段模型来减轻域的依赖性。
Discourse analysis and discourse parsing have shown great impact on many important problems in the field of Natural Language Processing (NLP). Given the direct impact of discourse annotations on model performance and interpretability, robustly extracting discourse structures from arbitrary documents is a key task to further improve computational models in NLP. To this end, we present a new, supervised paradigm directly tackling the domain adaptation issue in discourse parsing. Specifically, we introduce the first fully supervised discourse parser designed to alleviate the domain dependency through a staged model of weak classifiers by introducing the gradient boosting framework.
