E vent P lus: A Temporal Event Understanding Pipeline (2021)

Abstract: We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction. Event information, especially event temporal knowledge, is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. EventPlus as the first comprehensive temporal event understanding pipeline provides a convenient tool for users to quickly obtain annotations about events and their temporal information for any user-provided document. Furthermore, we show EventPlus can be easily adapted to other domains (e.g., biomedical domain). We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.

## introduction event understanding is intuitive for humans and important for daily decision making. for example, given the raw text shown in figure 1 , a person can infer lots of information including event trigger and type, event related arguments (e.g., agent, patient, location), event duration and temporal relations between events based on the linguistic and common sense knowledge. these understandings help people comprehend the situation and prepare for future events. the event and temporal knowledge are helpful for many downstream applications including question answering @xcite , story generation @xcite @xcite , and forecasting @xcite @xcite . ## component in this section, we introduce each component in our system, as shown in figure 3 . we use a multitask learning model for event trigger and temporal relation extraction ( § 2.1). the model introduced in § 2.2 extracts semantic-rich events following the ace ontology, and the model introduced in § 2.3 predicts the event duration. note that our system handles two types of event representations: one represents an event as the trigger word @xcite (as the event extraction model in § 2.1), the other represents event as a complex structure including trigger, type and arguments @xcite (as the event extraction model in § 2.2). the corpus following the former definition usually has a broader coverage while the latter can provide richer information. therefore, we develop models to combine the benefits of both worlds. we also introduce a speculated and negated events handling component in § 2.4 to further identify whether an event happens or not. ## system we design a pipeline system to enable the interaction among components with state-of-the-art performance introduced in § 2 and provide a comprehensive output for events and visualize the results. figure 3 shows the overall system design. ## evaluation each capability in the pipeline has its own input and output protocol, and they require various datasets to learn implicit knowledge independently. in this section, we describe the performance for each capability on corresponding labeled datasets. ## extension to biomedical domain with our flexible design, each component of event-plus can be easily extended to other domains with little modification. we explore two approaches to extend the event extraction capability ( § 2.2) to the biomedicine domain: 1) multi-domain training (mdt) with genia @xcite , a dataset containing biomolecular interaction events from scientific literature, with shared token embeddings, which enables the model to predict on both news and biomedical text; 2) replace the current component with an in-domain event extraction component scibert-ft @xcite which is a biomedical event extraction system based on fine-tuned scibert @xcite . while mdt on ace and genia datasets from different domains improves the performance on genia, it is still lower than scibert-ft (figure 4 ). therefore, we decide to pursue the second extension approach to incorporate scibert-ft and extend eventplus to the biomedical domain. ## related works existing nlp toolkits @xcite provide an interface for a set of useful models. some tools integrate several models in a pipeline fashion @xcite . the majority of these systems focus on token-level tasks like tokenization, lemmatization, part-of-speech tagging, or sentence-level tasks like syntactic parsing, semantic role labeling etc. there are only a few systems that can provide capabilities of event extraction and temporal information detection @xcite . for event extraction, some systems only provide results within a certain defined ontology such as aida @xcite , there are also some works utilizing data from multiple modalities @xcite . some works could handle novel events @xcite @xcite ), but they are either restricted to a certain domain @xcite or lack of performance superiority because of their lexico-syntactic rule-based algorithm @xcite . for temporal information detection, @xcite proposes a neural-based temporal relation extraction system with knowledge injection. most related to our work, @xcite demonstrates a temporal understanding system to extract time expression and implicit temporal relations among detected events, but this system cannot provide event-related arguments, entities and event duration information. these previous works either are not capable of event understanding or just focus on one perspective of event-related features. there is no existing system that incorporates a comprehensive set of event-centric features, including event extraction and related arguments and entities, temporal relations, and event duration. ## conclusion and future work we represent eventplus, a pipeline system that takes raw texts as inputs and produces a set of temporal event understanding annotations, including event trigger and type, event arguments, event duration and temporal relations. to the best of our knowledge, eventplus is the first available system that provides such a comprehensive set of temporal event knowledge extraction capabilities with state-of-the-art components integrated. we believe eventplus will provide insights for understanding narratives and facilitating downstream tasks. in the future, we plan to further improve event-plus by tightly integrating event duration prediction and temporal relation extraction modules. we also plan to improve the performance for triggers and arguments detection under the ace ontology and develop joint training models to optimize all eventrelated features in an end-to-end fashion.