论文标题
事件事件关系提取的联合限制学习
Joint Constrained Learning for Event-Event Relation Extraction
论文作者
论文摘要
理解自然语言涉及认识多个事件在结构和时间上如何相互互动。在此过程中，人们可以诱导事件复合体，这些事件复合物组织了多个一个处依恋事件，并在其中交织在一起。由于缺乏这些关系现象的共同标记的数据以及对它们所表达的结构的限制，我们提出了一个共同约束的学习框架，以建模事件事件 - 事件 - 事件关系。具体而言，该框架通过将这些约束转换为可区分的学习目标，从而在多个时间和下事物关系内部和跨越多个时间和子事件关系实施逻辑约束。我们表明，我们的共同限制学习方法有效地弥补了缺乏共同标记的数据，并且在基准上胜过SOTA方法的时间关系提取和事件层次结构构建，取代了常用但更昂贵的全球推断过程。我们还提出了一个有希望的案例研究，显示了我们方法在诱导外部语料库诱导事件络合物中的有效性。
Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. In this process, one can induce event complexes that organize multi-granular events with temporal order and membership relations interweaving among them. Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations. Specifically, the framework enforces logical constraints within and across multiple temporal and subevent relations by converting these constraints into differentiable learning objectives. We show that our joint constrained learning approach effectively compensates for the lack of jointly labeled data, and outperforms SOTA methods on benchmarks for both temporal relation extraction and event hierarchy construction, replacing a commonly used but more expensive global inference process. We also present a promising case study showing the effectiveness of our approach in inducing event complexes on an external corpus.
