Weakly Supervised Subevent Knowledge Acquisition

Abstract

Subevents elaborate an event and widely exist in event descriptions. Subevent knowledge is useful for discourse analysis and event-centric applications. Acknowledging the scarcity of subevent knowledge, we propose a weakly supervised approach to extract subevent relation tuples from text and build the first large scale subevent knowledge base. We first obtain the initial set of event pairs that are likely to have the subevent relation, by exploiting two observations that 1) subevents are temporally contained by the parent event, and 2) the definitions of the parent event can be used to further guide the identification of subevents. Then, we collect rich weak supervision using the initial seed subevent pairs to train a contextual classifier using BERT and apply the classifier to identify new subevent pairs. The evaluation showed that the acquired subevent tuples (239K) are of high quality (90.1% accuracy) and cover a wide range of event types. The acquired subevent knowledge has been shown useful for discourse analysis and identifying a range of event-event relations 1 .

OntoED: Low-resource Event Detection with Ontology Embedding

“…Besides, we utilize a multi-faceted event-event relation set R = R H R T R C for event ontology population and learning. Thereinto, R H = {SUBSUPER, SUPERSUB, COSUPER 1 } denotes a set of relation labels defined in the subevent relation extraction task (Wang et al, 2020a;Yao et al, 2020). R T = {BEFORE, AFTER, EQUAL 2 } denotes a set of temporal relations (Han et al, 2020).…”

ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning

“…Our definition of CO-REFERENCE is nearly identical as HIEVE where two co-referred events denote the same real-world events. Yao et al (2020) utilized a weakly-supervised method to extract large scale SUB-EVENT pairs, but the extracting rules can result in noisy relations.…”

“…In this work, we study five types of event semantic relations: CAUSAL, SUB-EVENT, CO-REFERENCE, CONDITIONAL and COUNTERFAC-TUAL. Though previous works study these relations such as SUB-EVENT (Glavaš et al, 2014;Yao et al, 2020), CAUSAL and CONDITIONAL O'Gorman et al, 2016), most of them adopted the pairwise relation extraction (RE) formality by constructing samples as (event trigger, event trigger, relation) triplets. There are two shortcomings of such RE formalism: 1) an event trigger word is used to represent the entire event, which could cause ambiguity; 2) relations are rigidly defined as class labels based on expert knowledge.…”

Learning Constraints and Descriptive Segmentation for Subevent Detection

“…Nevertheless the features often require costly human effort to obtain, and are often dataset-specific. Data-driven methods, on the other hand, automatically characterize events with neural language models like BERT (Devlin et al, 2019), and can simultanously incorporate various signals such as event time duration (Zhou et al, 2020), joint constraints with event temporal relations and subevent knowledge (Yao et al, 2020). Among recent methods, only Aldawsari and Finlayson (2019) utilize discourse features like discourse relations between elementary discourse units, but still document-level segmentation signals are not incorporated into the task of subevent detection.…”

“…As can be seen in the paragraph, though we cannot deny the existence of cross-segment subevent relations (dotted arrows), events belonging to the same membership are much more often to co-occur in a text segment. This correlation has been overlooked by existing data-driven methods (Zhou et al, 2020;Yao et al, 2020), which formulate subevent detection as pairwise relation extraction. On the other hand, while prior studies have demonstrated the benefits of incorporating logical constraints among event memberships and other relations (such as coreference) , the constraints between the memberships and event co-occurences in text segments remain uncertain.…”