Event Extraction as Machine Reading Comprehension (2020)

Abstract: Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Previous methods for EE typically model it as a classification task, which are usually prone to the data scarcity problem. In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC). Our approach includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERT-based question-answering process to retrieve answers as EE results. This learning paradigm enables us to strengthen the reasoning process of EE, by introducing sophisticated models in MRC, and relieve the data scarcity problem, by introducing the large-scale datasets in MRC. The empirical results show that: i) our approach attains state-of-the-art performance by considerable margins over previous methods. ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49.8% in F1 for event argument extraction with only 1% data, compared with 2.2% of the previous method. iii) Our model also fits with zero-shot scenarios, achieving 37.0% and 16% in F1 on two datasets without using any EE training data.

## introduction event extraction (ee), a crucial information extraction (ie) task, aims to extract event information in texts. for example, in a sentence s1 (shown in figure 1 (a) ), an ee system should recognize an attack event 1 , expressed by an event trigger stabbed with four event arguments -sunday (@xmath0, a protester (@xmath1, an officer (@xmath2, and a paper cutter (@xmath3. ee is shown to benefit a wide range of applications including knowledge 1 according to the ace event ontology. base augmentation @xcite , document summarization, question answering @xcite , and others. in the current study, ee is mostly formulated as a classification problem, aiming to locate and categorize each event trigger/argument @xcite @xcite . despite many advances, classification based methods are data-hungry, which require a great deal of training data to ensure good performance @xcite @xcite . moreover, such methods generally cannot deal with new event types never encountered during training time @xcite . in this particular study, we introduce a new learning paradigm for ee, shedding lights on tackling the above problems simultaneously. our major motivation is that, essentially ee may be viewed as a machine reading comprehension (mrc) problem @xcite ) involving text understanding and matching, aiming to find event-specific information in texts. for example, in s1, the extraction of role-filler of instrument is semantically equivalent to the following questionanswering process (as shown in figure 1 (b) ): q1: what instrument did the protester use to stab the officer? a1: a paper cutter. 2 this implies new ways to tackle ee, which come with two major advantages: first, by framing ee as mrc, we can leverage the recent advances in mrc (e.g., bert @xcite ) to boost ee task, which may greatly strengthen the reasoning process in the model. second, we may directly leverage the abundant mrc datasets to boost ee, which may relieve the data scarcity problem (this is referred to as cross-domain data augmentation). the second advantage also opens a door for zero-shot ee: for unseen event types, we can list questions defining their schema and use an mrc model to retrieve answers as ee results, instead of obtaining training data for them in advance. to bridge mrc and ee, the key challenge lies in generating relevant questions describing an event scheme (e.g., generating q1 for instrument). note we cannot adopt supervised question generation methods @xcite @xcite , owing to the lack of aligned question-event pairs. previous works connecting mrc and other tasks usually adopt humandesigned templates @xcite @xcite @xcite . for example, in qa-srl @xcite , the question for a predicate publish is always "who published something?", regardless of the contexts. such questions may not expressive enough to instruct an mrc model to find answers. we overcome the above challenge by proposing an unsupervised question generation process, which can generate questions that are both relevant and context-dependent. specifically, in our approach, we assume that each question can be decomposed as two parts, reflecting query topic and context-related information respectively. for example, q1 can be decomposed as "what instrument" and "did the protester use to stab the officer?". to generate the query topic expression, we design a template-based generation method, combining role categorization and interrogative words realization. to generate the more challenging contextdependent expression, we formulate it as an unsupervised translation task @xcite (or style transfer @xcite ), which transforms a descriptive statement into a questionstyle expression, based on in-domain de-noising auto-encoding @xcite and crossdomain back-translation @xcite . note the training process only needs large volume of descriptive statements and unaligned questionstyle statements. finally, after the questions are generated, we build a bert based mrc model @xcite to answer each of question and synthesize all of the answers as the result of ee. to evaluate our approach, we have conducted extensive experiments on the benchmark ee datasets, and the experimental results have justified the effectiveness of our approach. specifically, 1) in the standard evolution, our method attains state-ofthe-art performance and outperforms previous ee methods by a margin ( § 4.2). 2) in the data-low scenario, our approach demonstrates promising results, for example, achieving 49.8% in f1 using 1% of training data, compared with only 2.2% in f1 of the previous ee method ( § 4.3). 3) our approach also fits with zero-shot scenarios, achieving 37.0% and 16.6% in f1 on two datasets without using any ee training data ( § 4.4). to sum up, we make the following contributions: traditional ee methods employ manual-designed features, such as the syntactic feature @xcite , document-level feature @xcite , entity-level feature @xcite and other features @xcite ) for the task. modern ee methods employ neural models, such as convolutional neural networks @xcite , recurrent neural networks @xcite , graph convolutional neural networks @xcite , and other advanced architectures @xcite @xcite @xcite . despite many advances, as mentioned in introduction, most previous approaches formulate ee as a classification problem, which usually suffer from the data scarcity problem, and they generally cannot deal with new event types never seen at the training time. mrc for other tasks. our work also relates to works connecting mrc and other tasks, such as relation extraction @xcite , semantic role labeling @xcite , named entity recognition @xcite , and others @xcite . particularly, du and cardie (2020) adopt a similar idea to frames ee as mrc. but different from our work, most of the above methods @xcite @xcite adopt human-designed, context-independent questions, which may not provide enough contextual evidence for question-answering. some works indeed do not adopt question-style queries @xcite . for example, @xcite use "find organizations in the text" as a query command to find organization entity. the discrepancy between such non-natural "queries" and natural questions in mrc datasets may hinder effective transfer learning from mrc to the task. by contrast, our work aims to generate both relevant and context-related questions via an unsupervised question generation method. ## conclusion and future work in this paper, we take a fresh look at ee by casting it as an mrc problem. our method includes an unsupervised question generation process which can generate both relevant and context-related questions, whose effectiveness is verified by empirical results. in the future, we would adapt our method to other ie tasks to study its application scope.