论文标题
结构化跨度选择器
A Structured Span Selector
论文作者
论文摘要
许多自然语言处理任务，例如核心解决方案和语义角色标签，都需要选择文本跨度并就其做出决定。执行此类任务的典型方法是为所有可能的跨度评分，并贪婪地选择特定于任务的下游处理的跨度。但是，这种方法并未纳入有关应选择哪种跨度的诱导偏见，例如，选定的跨度倾向于是句法成分。在本文中，我们提出了一种新型的基于语法的结构化跨度选择模型，该模型学会利用为此类问题提供的部分跨度注释。与以前的方法相比，我们的方法摆脱了启发式贪婪的跨度选择方案，使我们能够在一组最佳跨度上对下游任务进行建模。我们在两个流行的跨度预测任务上评估了模型：核心分辨率和语义角色标签。我们对两者都展示了经验改进。
Many natural language processing tasks, e.g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them. A typical approach to such tasks is to score all possible spans and greedily select spans for task-specific downstream processing. This approach, however, does not incorporate any inductive bias about what sort of spans ought to be selected, e.g., that selected spans tend to be syntactic constituents. In this paper, we propose a novel grammar-based structured span selection model which learns to make use of the partial span-level annotation provided for such problems. Compared to previous approaches, our approach gets rid of the heuristic greedy span selection scheme, allowing us to model the downstream task on an optimal set of spans. We evaluate our model on two popular span prediction tasks: coreference resolution and semantic role labeling. We show empirical improvements on both.
