Semi-supervised New Event Type Induction and Event Detection (2020)

Abstract: Most previous event extraction studies assume a set of target event types and corresponding event annotations are given, which could be very expensive. In this paper, we work on a new task of semi-supervised event type induction, aiming to automatically discover a set of unseen types from a given corpus by leveraging annotations available for a few seen types. We design a Semi-Supervised Vector Quantized Variational Autoencoder framework to automatically learn a discrete latent type representation for each seen and unseen type and optimize them using seen type event annotations. A variational autoencoder is further introduced to enforce the reconstruction of each event mention conditioned on its latent type distribution. Experiments show that our approach can not only achieve state-of-the-art performance on supervised event detection but also discover high-quality new event types.

## introduction event extraction is a task of automatically identifying and typing event trigger words (event detection), and extracting participants for each trigger (argument extraction) from natural language text. traditional event extraction studies @xcite @xcite @xcite @xcite @xcite usually assume there exists a set of predefined event types and argument roles, so that supervised machine learning models, e.g., deep neural networks, can be employed to extract events for each type based on human annotations. however, in practice, it is usually very expensive and time-consuming to manually craft an event schema, which defines the types and complex templates of the expected events. moreover, the coverage of manually crafted schemas is often very low, making them fail to generalize to new scenarios. recent studies have shown that it's possible to automatically induce an event schema from raw text. some researchers explore probabilistic generative methods @xcite @xcite or ad-hoc clustering-based algorithms @xcite to discover a set of event types and argument roles. several studies @xcite also explore zero-shot and few-shot learning approaches to leverage available resources and extend event extraction to new types. generally, event schema induction can be divided into two steps: event type induction, aiming to discover a set of new event types for the given scenario, and argument role induction which discovers a set of argument roles for each type. in this work, we focus on tackling the first problem only. we propose a task of semi-supervised event type induction, which is shown in figure 1 and aims to leverage available event annotations for a few types, which are called as seen types, and automatically discover a set of new unseen types, as well as their corresponding event mentions. as a solution, we design a new semi-supervised vector quantized variational autoeocoder framework (short as ss-vq-vae) which first assigns a discrete latent type representation for each seen and unseen type, and optimizes them during the process of projecting each candidate trigger into a particular seen or unseen type. the candidate triggers are discovered with a heuristic approach. experiments under the setting of both supervised event detection and new event type induction demonstrate that our approach can not only detect event mentions for seen types with high precision, but also discover high-quality new unseen types. ## approach e ayman ayman was arrested and was sentenced to life in prison . e ayman e was trigger representation e was e arrested ayman was arrested and was sentenced to life in prison. e and e was e sentenced e to e life e in eprison encoder fe(v t ) encoder f c (v t ) decoder fr(z t , y t ) linear linear y @xmath0... ... ## related work traditional event extraction studies @xcite @xcite @xcite @xcite @xcite ) assume all the target event types and annotations are given. they can extract high-quality event mentions for the given types, but cannot extract mentions for any new types. recent studies @xcite @xcite leverage annotations for a few seen event types or several keywords provided for the new types to extract mentions for new types. however, all these studies assume all the target types are given, which is very costly when moving to a new scenario. recent studies have explored probabilistic generative methods @xcite @xcite or ad-hoc clustering based algorithms @xcite to automatically discover a set of event types as well as argument roles. most of these studies are completely unsupervised and mainly rely on statistical patterns or semantic matching, while our work tries to leverage the knowledge learned from available annotations to discover new event types. ## conclusion and future work we have designed a semi-supervised vector quantized variational autoencoder approach which automatically learns a discrete representations for each seen and unseen type and predict a type for each candidate trigger. experiments show that our approach achieves the state-of-the-art on supervised event extraction and discovers a set of high-quality unseen types. in the future, we will extend this approach to argument role induction to discover complete event schemas.