论文标题
一个简单的全球神经话语解析器
A Simple Global Neural Discourse Parser
论文作者
论文摘要
话语解析在很大程度上由具有手动设计特征的贪婪解析器主导，而由于其计算费用，全球解析很少见。在本文中，我们提出了一个简单的基于图表的神经话语解析器，该解析器不需要任何手动制作的功能，并且仅基于学习的跨度表示。为了克服计算挑战，我们提出了分配给树节点的标签与将其子分离的分裂点之间的独立性假设，从而导致可拖动的解码。我们从经验上证明，我们的模型在全球解析器中实现了最佳性能，并且仅使用学习的跨度表示，与最先进的贪婪解析器相当。
Discourse parsing is largely dominated by greedy parsers with manually-designed features, while global parsing is rare due to its computational expense. In this paper, we propose a simple chart-based neural discourse parser that does not require any manually-crafted features and is based on learned span representations only. To overcome the computational challenge, we propose an independence assumption between the label assigned to a node in the tree and the splitting point that separates its children, which results in tractable decoding. We empirically demonstrate that our model achieves the best performance among global parsers, and comparable performance to state-of-art greedy parsers, using only learned span representations.
