Event Time Extraction and Propagation via Graph Attention Networks (2021)

Abstract: Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work. This problem is challenging due to the inherent ambiguity of language and the requirement for information propagation over inter-related events. This paper first formulates this problem based on a 4-tuple temporal representation used in entity slot filling, which allows us to represent fuzzy time spans more conveniently. We then propose a graph attention network-based approach to propagate temporal information over document-level event graphs constructed by shared entity arguments and temporal relations. To better evaluate our approach, we present a challenging new benchmark on the ACE2005 corpus, where more than 78% of events do not have time spans mentioned explicitly in their local contexts. The proposed approach yields an absolute gain of 7.0% in match rate over contextualized embedding approaches, and 16.3% higher match rate compared to sentence-level manual event time argument annotation.

## introduction understanding and reasoning about time is a crucial component for comprehensive understanding of evolving situations, events, trends and forecasting event abstractions for the long-term. event time extraction is also useful for many downstream natural language processing (nlp) applications such as event timeline generation @xcite @xcite , temporal event tracking and prediction @xcite , and temporal question answering @xcite . in order to ground events into a timeline we need to determine the start time and end time of each event as precisely as possible @xcite . however, the start and end time of an event is often not explicitly expressed in a document. for example, among 5,271 annotated event mentions in the automatic content extraction (ace2005) corpus foot_1 , only 1,100 of them have explicit time argument annotations. to solve the temporal event grounding (teg) problem, previous efforts focus on its subtasks such as temporal event ordering @xcite @xcite @xcite @xcite @xcite and duration prediction @xcite @xcite @xcite . in this paper we aim to solve teg directly using the following novel approaches. to capture fuzzy time spans expressed in text, we adopt a 4-tuple temporal representation proposed in the tac-kbp temporal slot filling task @xcite to predict an event's earliest possible start date, latest possible start date, earliest possible end date and latest possible end date, given the entire document. we choose to work at the daylevel and leave time scales smaller than that for future work since, for example, only 0.6% of the time expressions in the newswire documents in ace contain smaller granularities (e.g., hours or minutes). fortunately, the uncertain time boundaries of an event can often be inferred from its related events in the global context of a document. for example, in ## conclusions and future work in this paper, we have created a new benchmark for document-level event time extraction based on 4-tuple representation, which provides rich representation to handle uncertainty. we propose a graph-based time propagation and use event-event relations to construct document-level event graphs. our experiments and analyses show the effectiveness of our model. in the future, we will focus on improving the fundamental pretraining model for time to represent more fine-grained time information and cross-document temporal aggregation.