Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection (2022)

Abstract: Prototypical network based joint methods have attracted much attention in few-shot event detection, which carry out event detection in a unified sequence tagging framework. However, these methods suffer from the inaccurate prototype representation problem, due to two main reasons: the number of instances for calculating prototypes is limited; And, they do not well capture the relationships among event prototypes. To deal with this problem, we propose a Knowledge-Enhanced self-supervised Prototypical Network, called KE-PN, for few-shot event detection. KE-PN adopts hybrid rules, which can automatically align event types to an external knowledge base, i.e., FrameNet, to obtain more instances.It proposes a self-supervised learning method to filter out noisy data from enhanced instances. KE-PN is further equipped with an auxiliary event type relationship classification module, which injects the relationship information into representations of event prototypes. Extensive experiments on three benchmark datasets, i.e., FewEvent, MAVEN, and ACE2005 demonstrate the state-of-the-art performance of KE-PN.

## introduction event detection is fundamental to information extraction, which consists of two sub-processes, i.e., trigger word identification and event classification. the former extracts the triggers from a piece of text describing events, while the latter classifies them into different event types. for example , in "our college is to make arrangements for the meeting", the trigger words are "make arrangements", indicating an arranging event. event detection benefits many downstream applications, e.g., question answering and information retrieval. typical methods for event detection @xcite @xcite ., * *corresponding author. there are two kinds of approaches in fsed, namely, pipeline ones @xcite @xcite and joint ones @xcite @xcite . the former adopt a two-stage (i.e., identification and classification) process, while the latter regard the two sub-processes as a joint one. since the joint approaches alleviate the error propagation problem appeared in pipeline ones, they have become the mainstream ones. in the joint approaches, fsed is formulated as a sequence tagging task, where each word in a sequence is assigned a label. the label consists of two parts: the position part and the type part @xcite . there are three types of labels for the position part, i.e., b, i and o, where b and i indicate the beginning and inside positions of the corresponding words in the event triggers, respectively, and o refers to other words (i.e., non-trigger ones). the type part indicates the event type of the instance. moreover, this kind of approaches usually adopts prototypical network (pn) @xcite as their classifier, whose main idea is to learn a metric space in which classification of an instance can be performed by measuring its distance to different prototypes. an example of "bio"-based sequence tagging pn for fsed is shown in figure 1 . furthermore, the b-arranging and i-arranging prototypes compose the arranging event prototype. a challenging problem of these pn-based fsed approaches is how to obtain accurate prototype representations, because of two main reasons: first, the number of instances for calculating event prototypes is limited in the few-shot scenarios. in fsed, the trigger words account for only a small proportion of all tokens in a word sequence, which makes the tokens with labels b and i even fewer. hence, the prototype representations for labels b and i become less accurate. second, existing approaches usually assume by default that event prototypes are independent. therefore, they fail to capture the relationships (i.e., the parent-child relationship and the sibling relationship) among these prototypes. to solve the above problem, we propose a novel knowledge-enhanced self-supervised prototypical network, called ke-pn, for fsed. to obtain more accurate prototype representations, ke-pn adopts a novel knowledge enhancement method which introduces knowledge from an external knowledge base, i.e., framenet @xcite , when computing the prototypes. unlike recent approaches relying on a mixture of string matching and human annotation @xcite , ke-pn applies hybrid rules which align event types to the frames in framenet via a completely automatic manner. then, ke-pn replaces the triggers of the support instances with the lexiunits in the aligned frames to form new instances. to reduce the noise brought by the above method, ke-pn adopts a self-supervised learning method to filter out noise from the enhanced support set. moreover, so as to inject relationship information into prototype representations, ke-pn is equipped with an auxiliary event type relationship classification module. in summary, the main contributions of this paper are three-fold. ## notations in fsed, two datasets are given: d train and d test , which have disjoint event type sets. each dataset contains several tasks and each task consists of a support set and a query set, which is formulated in the n -way k-shot paradigm. given the support set ## the ke-pn method the ke-pn method consists of three modules, i.e., representation learning, event detection and event type relationship classification, as shown in figure 2 . representation learning. this module aims to obtain the representations of event prototypes and query instances. to obtain accurate prototype representations, a knowledge enhancement self-supervised learning method is applied. event detection. this module takes the representations of prototypes and query instances as its input, and predicts the labels of tokens in the query set. we adopt pa-crf, which is the state-of-the-art fsed model, as the event detection method. event type relationship classification. this module takes the prototype representations as its input, and predicts whether two prototypes have concerned relationships. this module injects relationship information into prototype representations by working as an auxiliary module. no. alignment conditions 1 ti.eql(f ) 2 for any ten in p(ti): ten.eql(f ) 3 for any nou in n(ti): nou.eql(f ) 4 for any syn in s(ti): syn.eql(f ) 5 ti.con(f ) or f .con(ti) 6 for any ten in p(ti): ten.con(f ) or f .con(ten) 7 for any nou in n(ti): nou.con(f ) or f .con(nou) 8 for any syn in s(ti): syn.con(f ) or f .con(syn) ## conclusion and future work in this paper, we proposed a novel knowledgeenhanced self-supervised prototypical network, called ke-pn, for fsed. ke-pn proposes hybrid rules which align the event types to framenet and then introduces knowledge to obtain more instances. furthermore, ke-pn presents a novel self-supervised learning method to filter out noise from enhanced instances. moreover, ke-pn adopts event type relationship classification as an auxiliary module, to inject relationship information into prototype representations. extensive experiments on three benchmark fsed datasets, i.e., fewevent, maven and ace2005, demonstrate the state-ofthe-art performance of ke-pn. in the future work, we will explore fsed into a lifelong learning architecture, as the continuous fsed is an important problem in the real world.