论文标题
亚事件序列预测的类似过程结构诱导
Analogous Process Structure Induction for Sub-event Sequence Prediction
论文作者
论文摘要
事件理解的计算和认知研究表明，识别，理解和预测事件取决于具有一系列事件的结构化表示以及概念化（抽象）其组件中的（软）事件类别。因此，有关“购买汽车”之类的已知过程的知识可以在新的但类似的过程（例如“购买房屋”）的背景下使用。然而，大多数理解NLP中的事件工作仍处于地面水平，并且不考虑抽象。在本文中，我们提出了一个类似的过程结构诱导APSI框架，该框架利用了过程之间的类比和子事实实例的概念化，以预测以前看不见的开放式域过程的整个子事实序列。正如我们的实验和分析所表明的那样，APSI支持在看不见的过程中生成有意义的子事件序列，并可以帮助预测缺失的事件。
Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories. Thus, knowledge about a known process such as "buying a car" can be used in the context of a new but analogous process such as "buying a house". Nevertheless, most event understanding work in NLP is still at the ground level and does not consider abstraction. In this paper, we propose an Analogous Process Structure Induction APSI framework, which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub-event sequence of previously unseen open-domain processes. As our experiments and analysis indicate, APSI supports the generation of meaningful sub-event sequences for unseen processes and can help predict missing events.
