CAREER: Developing an Underspecified Representation for Temporal Information in Text

尽管最近在自然语言自动处理方面取得了进展，但在许多情况下，接近人类对文本的一般水平的理解仍然具有挑战性。这个职业项目解决了一个这样的挑战，自动提取和理解自然语言文本叙述中描述的事件的顺序和时间。它开发了一个表示和提取文本中所传达的时间信息的计算框架，最终目标是能够从文本中进行现实的时间推理。与已有的研究方法不同的是，该研究假设时间描述不足是时间表征的一个固有属性，并由粗粒度事件簇的概念支持。它通过识别事件簇及其叙事锚来利用叙事文本的微观结构，这些事件簇及其叙事锚与默认的事件时间和持续时间一起，用于组织时间线的未指定表示。它还通过开发三个关键组件来解决当前最先进的知识差距。首先，采用了一种新颖的表征方案，便于注释者进行简单、直观的选择，从而最大限度地减少认知努力，减少标注错误。问答作为目标应用程序，重点放在阅读理解问题上，这些问题需要时间推理，而不是理解简单的事实。第二，使用新的注释一致性内在评估方法来解决现有评估方法存在的问题，这些评估方法通常会产生不同的结果，这取决于对通过时间关系图上的传递闭包所推断的时间关系的具体信任策略。所提出的方法依赖于将时态关系表示为偏序集，并使用这些偏序的线性扩展来评估注释者之间的一致性和系统性能。最后，探索了一类新的基于神经网络的模型，该模型旨在恢复锚定事件簇上的偏序图。这些模型使用外部记忆组件进行累积语篇表示，并允许联合训练以识别共指事件提及，将恢复的事件分组为大致同时发生的事件簇，并在它们之间建立类型化链接。这些模型包括默认事件顺序和时间的表示，以及事件的参数结构和对时间表达式的定量推理。

Despite the recent advances in automated processing of natural language, approaching general human-level understanding of text in many cases still remains challenging. This CAREER project addresses one such challenge, automatically extracting and understanding the order and timing of events described in natural language text narratives. It develops a computational framework for representing and extracting temporal information conveyed in text, with the end goal to enable realistic temporal reasoning from text. It also engages student involvement in computer science research early on, and in particular is designed to attract female students to pursue careers in computer science and related areas.As distinct from existing approaches, the proposed research assumes underspecification to be an integral property of temporal representation, supported by the notion of a coarse-grained event cluster. It takes advantage of the micro-structure of narrative text by identifying event clusters and their narrative anchors which, together with default event times and durations, serve to organize the underspecified representation of the timeline. It also addresses knowledge gaps in the current state-of-the-art in by developing three key components. First, a novel representational scheme is employed to facilitate simple, intuitive choices for the annotators that minimize cognitive effort and reduce annotation error. Question answering serves as the target application, with a focus on reading-comprehension questions that require temporal reasoning beyond understanding simple factoids. Second, novel methods for intrinsic evaluation of annotation consistency are used to address problems with existing evaluation methods, which often produce varied results depending on specific strategies for crediting temporal relations inferred through transitive closure over the temporal relation graph. The proposed methods rely on representing temporal relations as partially ordered sets and use linear extensions of these partial orders in order to evaluate inter-annotator agreement and system performance. Finally, a new class of neural network-based models is explored that aim to recover partial order graphs over anchored event clusters. These models use external memory components for cumulative discourse representation, and allow joint training for identifying coreferent event mentions, grouping the recovered events into roughly-simultaneous event clusters, and establishing typed links between them. The models incorporate a representation for default event order and timing, as well as argument structure for events and quantitative inference over temporal expressions.