Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction (2021)

Abstract: Event time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20% of event mentions in TimeBank-Dense have event-time links. In this paper, we propose a joint model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. We adopt the Stack-Propagation framework to incorporate predicted relative event time for temporal relation classification and keep the differentiability. Our experiments on MATRES dataset show that our model can significantly improve the RoBERTa-based baseline and achieve state-of-the-art performance.

## introduction event temporal ordering is an important task to understand the evolution of events. event-event temporal relation extraction aims to automatically extract the temporal order given a pair of events and further build a temporal graph. figure 1 illustrates an example sentence and its temporal graph. there are three events in the sentence, said, identified and run. the temporal relation between said and identified is after, and the temporal relations between said and run and between identified and run are before. neural network based methods have achieved promising improvement for temporal relation extraction @xcite @xcite @xcite . they mostly consider the task as pairwise classification. there are also efforts focusing on the global structures, including markov logical networks and integer linear programming (ilp) based methods @xcite @xcite @xcite @xcite . though achieving great success, event time, an important feature, is often overlooked by previous work. conceptually, if we know the exact time information for all events, their temporal relations can be naturally derived. for example, if we know that event a happened on monday while event b happened on tuesday in the same week, it is obvious that a happened before b. however, explicit time arguments can be rarely found in text, especially in news articles @xcite . @xcite propose to predict the relative timeline and directly compare the relative timestamps of events to derive their temporal relations. although showing promising performance, those predicted timestamps do not consider information from event pairs and cannot handle the uncertain temporal boundary of an event expressed in text to predict relations such as vague. in this paper, we follow the idea of @xcite to predict the relative event time for temporal relation extraction, where the relative time is a real number indicating the relative position of the event in the timeline. instead of directly comparing relative event time as leeuwenberg and moens (2018), we consider them as additional features and incorporate them into training the temporal relation classifier. we propose a joint model with stack-propagation framework @xcite to connect relative event time prediction and temporal relation extraction, which can exploit the explicit features from the former task for the latter task @xcite . our model directly uses the output of relative event time prediction as the input for temporal relation classification so that the classification can benefit from both representations of event pairs and the predicted relative event time. because we do not break the differentiability between two tasks, the training objective for one task can also promote another task. similarly to @xcite , we adapt margin-based optimization to constrain the distance between two relative event time values, given their temporal relation. our experiments show that the relative event time prediction can significantly help learn better temporal relation extraction, compared to vanilla roberta-based @xcite baseline. we have also achieved similar performance compared to the state-of-the-art temporal relation extraction approach that uses additional data @xcite . ## approach microsoft said it has identified three roberta encoding ... in this section, we will discuss the relative event time prediction for temporal relation extraction. the overall approach is illustrated in figure 2 . ## related work earlier efforts on temporal relation extraction focus on global inference using methods such as integer linear programming @xcite @xcite @xcite . recently, neural network-based methods have also achieved promising improvement @xcite @xcite @xcite @xcite . especially, @xcite use lstm to encode timex for temporal relation extraction, @xcite jointly train event time arguments extraction from ace2005. the most related work is @xcite , which proposes to predict relative event time and uses the comparison of relative timestamps as temporal relations. our work focuses on jointly training relative time prediction and temporal relation extraction and utilizes relative timestamps as features via the stack-propagation framework. ## conclusions and future work in this paper, we leverage relative event time prediction that can ground events onto a relative timeline to help event-event temporal relation extraction. we use stack-propagation framework to further incorporate predicted timestamp explicit for relation classification. our experiment results demonstrate the effectiveness of our proposed method. in the future, we will focus on extending the relative time prediction to cross-document setting and support cross-document temporal relation extraction.