Learning Constraints and Descriptive Segmentation for Subevent Detection (2021)

Abstract: Event mentions in text correspond to real-world events of varying degrees of granularity. The task of subevent detection aims to resolve this granularity issue, recognizing the membership of multi-granular events in event complexes. Since knowing the span of descriptive contexts of event complexes helps infer the membership of events, we propose the task of event-based text segmentation (EventSeg) as an auxiliary task to improve the learning for subevent detection. To bridge the two tasks together, we propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction, as well as guiding the model to make globally consistent inference. Specifically, we adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model. Experimental results show that the proposed method outperforms baseline methods by 2.3% and 2.5% on benchmark datasets for subevent detection, HiEve and IC, respectively, while achieving a decent performance on EventSeg prediction.

## introduction since real-world events are frequently conveyed in human languages, understanding their linguistic counterparts, i.e. event mentions in text, is of vital importance to natural language understanding (nlu). one key challenge to understanding event mentions is that they refer to real-world events with varied granularity @xcite and form event complexes @xcite . for example, when speaking of a coarse-grained event "publishing a paper", it can involve a complex of former penn state football coach jerry sandusky posted (e1) bail thursday after spending a night in jail following a new round of sex-abuse charges (e2) filed against him. sandusky secured his release using (e3) @xmath050,000 certified check provided (e4) by his wife, dorothy, according to online court record … he was also charged (e5) last month with abusing eight boys, some on campus, over 15 years, allegations that were not immediately brought to the attention of authorities even though high-level people at penn state apparently knew about them. in all, he faces more than 50 charges (e6). the scandal (e7) has resulted in the ousting (e8) of school president graham spanier and longtime coach joe paterno. figure 1 : an example of parent-child relations and eventsegs from the hieve dataset @xcite . the blue and yellow segments denote the textual spans of event complexes "posted" and "scandal" respectively. curved arrows denote parent-child relations within a text segment, whereas the dotted arrows denote cross-segment parent-child relations. more fine-grained events such as "writing the paper," "passing the peer review," and "presenting at the conference." naturally, understanding events requires to resolve the granularity of events and infer their memberships, which corresponds to the task of subevent detection (a.k.a. event hierarchy extraction). practically, subevent detection is a key component of event-centric nlu @xcite , and is beneficial to various applications, such as schema induction @xcite , task-oriented dialogue agents @xcite , summarization @xcite , and risk detection @xcite . as a significant step towards inducing event complexes (graphs that recognize the relationship of multi-granular events) in documents, subevent detection has started to receive attention recently @xcite . it is natu-ral to perceive that in documents, there might be several different event complexes and they often span in different descriptive contexts that form relatively independent text segments. consider the example in fig. 1 , where the two membership relations in the event complex (graph consisting of "scandal (e7)," "charges (e6)," "ousting (e8)," and relations) are both within the segment marked in yellow that describes the event complex. 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 @xcite , 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) @xcite , the constraints between the memberships and event co-occurences in text segments remain uncertain. hence, how to effectively learn and enforce hard-to-articulate constraints as in the case of subevent detection and segmentation of text is another challenge. our first contribution is to improve subevent detection based on an auxiliary task of eventseg prediction. by eventseg prediction, we seek to segment a document into descriptive contexts of different event complexes. evidently, with eventseg information, it would be relatively easy to infer the memberships of events in the same descriptive context. using annotations for subevent detection and eventseg prediction, we aim to adopt a neural model to jointly learn these two tasks along with the (soft) logical constraints that bridge their labels together. in this way, we incorporate linear discourse structure of segments into membership relation extraction, avoiding complicated feature engineering in the previous work @xcite . from the learning perspective, adding eventseg prediction as an auxiliary task seeks to provide effective incidental supervision signals @xcite to the subevent detection task. this is especially important in the current scenario where annotated learning resources for subevents are typically limited @xcite @xcite . to capture the logical dependency between subevent structure and eventseg, our second contribution is an approach to automatically learning and enforcing logical constraints. motivated by @xcite , we use rectifier networks to learn constraints in the form of linear inequalities, and then convert the constraints to a regularization term that can be incorporated into the loss function of the neural model. this allows any hard-to-articulate constraints to be automatically captured for interrelated tasks, and efficiently guides the model to make globally consistent inference. by learning and enforcing task-specific constraints for subevent relations, the proposed method achieves comparable results with sota subevent detection methods on the hieve and ic dataset. moreover, by jointly learning with eventseg prediction, the proposed method surpasses previous methods on subevent detection by relatively 2.3% and 2.5% in f 1 on hieve and ic, while achieving decent results on eventseg prediction. we discuss three lines of relevent research. ## methods we now present the framework for learning and enforcing constraints for the main task of subevent detection and the auxiliary eventseg prediction. we start with learning the hard-to-articulate constraints ( §4.1), followed by details of joint learning ( §4.2) and inference ( §4.3) for the two tasks. ## experiments here we describe the experiments on subevent detection with eventseg prediction as an auxiliary task. we first introduce the corpora used ( §5.1), followed by evaluation for subevent detection and an ablation study for illustrating the importance of each model component ( §5.2- §5.4). we also provide a case study on eventseg prediction ( §5.5) and an analysis of the constraints learned in the model ( §5.6). ## conclusion in this work we propose an automatic and efficient way of learning and enforcing constraints for subevent detection. by noticing the connections between subevent dection and eventseg, we adopt eventseg prediction as an auxiliary task which provides effective incidental supervision signals. through learning and enforcing constraints that can express hard-to-articulate constraints, the logical rules for both tasks are captured to regularize the model towards consistent inference. the proposed approach outperforms sota data-driven methods on benchmark datasets and provides comparable results with recent text segmentation methods on eventseg prediction. this demonstrates the effectiveness of the framework on subevent detection and the potential of solving other structured predictions tasks in nlp.