Weakly S upervised S ubevent K nowledge A cquisition (2020)

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.

## introduction a subevent is the event that happens as a part of the other event (i.e., parent event) spatio-temporally @xcite . subevents, which elaborate and expand an event, widely exist in event descriptions. for instance, when describing election events, people usually describe typical subevents such as "nominate candidates", "debates" and "people vote". knowing typical subevents of an event can help with analyzing several discourse relations (such as expansion and temporal relations) between text units. furthermore, knowing typical subevents of an event is important for understanding the internal structure of the event (what is the event about?) and its properties (is this a violent or peaceful event?), and therefore has great potential to benefit event detection, event tracking, event visualization and event summarization among many other applications. while being in high demand, little subevent knowledge can be found in existing knowledge bases. therefore, we aim to extract subevent knowledge from text and build the first subevent knowledge base covering a large number of commonly seen events and their rich subevents. little research has focused on identifying the subevent relation between two events in a text. several datasets annotated with subevent relations @xcite @xcite exist, but they are extremely small and usually contain dozens to one/two hundred documents. subevent relation classifiers trained on these small datasets are not suitable to use to extract subevent knowledge from text, considering that subevent relations can appear in dramatically different contexts depending on topics and events. we propose to conduct weakly supervised learning and train a wide-coverage contextual classifier to acquire diverse event pairs of the subevent relation from text. we start by creating weak supervision, where we aim to identify the initial set of subevent relation tuples from a text corpus. with no contextual classifier at the beginning, it is difficult to extract subevent relation tuples because subevent relations are rarely stated explicitly. instead, we propose a novel two-step approach to indirectly obtain the initial set of subevent relation tuples, exploiting two key observations that (1) subevents are temporally contained by the parent event, and thus can be extracted with linguistic expressions that in-dicate the temporal containment relationship foot_1 , and (2) the definition of the parent event is useful to prune spurious subevent tuples away to improve the quality. specifically, we first use several preposition patterns (e.g., e i during e j ) that indicate the temporal relation contained_by between events to identify candidate subevent relation tuples. then, we conduct an event definition-guided semantic consistency check to remove spurious subevent tuples that often include two temporally overlapping but semantically incompatible events. for example, a news article may report a bombing event that happened in parallel during a festival, but the intense bombing event is not semantically compatible with the entertaining event festival, as informed by the common definition of festival: a festival is an organized series of celebration events, or an organized series of concerts, plays, or movies, typically one held annually. next, we identify sentences from the text corpus that contain an event pair, and use these sentences to train a contextual classifier that can recognize the subevent relation in text. we train the contextual subevent relation classifier by fine-tuning the pretrained bert model @xcite . we then apply the contextual bert classifier to identify new event pairs that have the subevent relation. we have built a large knowledge base of 239k subevent relation tuples. the knowledge base contains subevents for 10,318 unique events, with each event associated with 20.1 subevents on average. intrinsic evaluation demonstrates that the learned subevent relation tuples are of high quality (90.1% of accuracy) and are valuable for event ontology building and exploitation. the learned subevent knowledge has been shown useful for identifying subevent relations in text, including both intra-instance and cross-sentence cases. in addition, the learned subevent knowledge is shown useful for identifying temporal and causal relations between events as well, for the challenging cross-sentence cases where we usually have little contextual clues to rely on. furthermore, when incorporated into a recent neural discourse parser, the learned subevent knowledge has noticeably improved the performance for identifying two types of implicit discourse relations, expansion and temporal relations. in short, we made three main contributions: 1). we developed a novel weakly supervised approach to acquire subevent knowledge from text. 2). we have built the first large scale subevent knowledge base that is of high quality and covers a wide range of event types. 3). we performed extensive evaluation showing that the harvested subevent knowledge not only improves subevent relation extraction, but also improves a wide range of nlp tasks such as causal and temporal relation extraction and discourse parsing. ## overview of the weakly supervised approach figure 1 shows the overview of the weakly supervised learning approach for subevent knowledge acquisition. the key of this approach is to identify seed event pairs that are likely to be of the subevent relation in a two-step procedure (section 4). we first use several temporal relation patterns (e.g., e i during e j ) to identify candidate seed pairs since a child event is usually temporally contained by its parent event; and then, we conduct a definitionguided semantic consistency check to remove spurious subevent pairs that are semantically incompatible and are unlikely to have the subevent relation, e.g., (festival, bombing). next, we find occurrences of seed pairs in a large text corpus to quickly generate many subevent relation instances, we will also create negative instances to train the subevent relation classifier (section 5). then, the trained contextual classifier will be used to identify new event pairs of the subevent relation by examining multiple occurrences of an event pair in text (section 6). we use the english gigaword @xcite as the text corpus. ## the contextual classifier using bert recently, bert @xcite pretrained on massive data has achieved high performance on various nlp tasks. we fine-tune a pretrained bert model to build the contextual classifier for subevent relation identification. bert model is essentially a bi-directional transformer-based encoder that consists of multiple layers where each layer has multiple attention heads. formally, given a sentence with n tokens, each attention head transforms a token vector t i into query, key, and value vectors q i , k i , v i through three linear transformers. next, for each token, the head calculates the self-attention scores for all other tokens of the input sentence against this token as the softmax-normalized dot products between two query and key vectors. the output o i of each attention head is a weighted sum of all value vectors: in this way, we can obtain n contextualized embeddings {o i } n @xmath0. to enforce the bert encoder to look at context information other than the two event trigger words of a subevent pair, e.g., war, person battle, we replace the two event trigger words in a sentence with a special token @xcite as the original bert model did for masking. the contextualized embeddings at two event triggers' positions (two [mask]'s positions) are concatenated and then fed into a feed-forward neural network with a softmax prediction layer for three-way classification, i.e., to predict two subevent relations (parent-child and child-parent relations depending on the textual order of two events) and no subevent relation (other). in our experiments, we use the pretrained bert base model provided by @xcite with 12 transformer block layers, 768 hidden size and 12 self-attention heads foot_7 . we train the classifier using cross-entropy loss and adam (kingma and ba, 2015) optimizer with initial learning rate 1e-5, 0.5 dropout, batch size 16 and 3 training epochs. ## identifying new subevent pairs we next apply the contextual bert classifier to identify new event pairs that express the subevent relation. it is unnecessary to test on all possible pairs of events since two random events that cooccur in a sentence have a small chance to have the subevent relation. in order to narrow down the search space, we first identify candidate event pairs that are likely to have the subevent relation. then, we apply the contextual classifier to examine instances of each candidate event pair in order to determine valid subevent relation pairs. ## conclusions we have presented a novel weakly supervised learning framework for acquiring subevent knowledge and built the first large scale subevent knowledge base containing 239k subevent tuples. evaluation showed that the acquired subevent pairs are of high quality (90.1% of accuracy) and cover a wide range of event types. we performed extensive evaluations showing that the harvested subevent knowledge not only improves subevent relation extraction, but also improve a wide range of nlp tasks such as causal and temporal relation extraction and discourse parsing. in the future, we would like to explore uses of the subevent knowledge base for other eventoriented applications such as event tracking.