Biomedical Event Extraction as Multi-turn Question Answering (2020)

Abstract: Biomedical event extraction from natural text is a challenging task as it searches for complex and often nested structures describing specific relationships between multiple molecular entities, such as genes, proteins, or cellular components. It usually is implemented by a complex pipeline of individual tools to solve the different relation extraction subtasks. We present an alternative approach where the detection of relationships between entities is described uniformly as questions, which are iteratively answered by a question answering (QA) system based on the domain-specific language model SciBERT. This model outperforms two strong baselines in two biomedical event extraction corpora in a Knowledge Base Population setting, and also achieves competitive performance in BioNLP challenge evaluation settings.

## introduction biomedical event extraction (bee) @xcite aims to extract molecular events from natural text, where an event typically encompasses certain biomedical entities, such as genes, proteins, complexes or cellular components, specific trigger words determining the event type, and relationships between the entities whose roles depends on the event type. for instance, the verb phosphorylates is a hint to a mention of a phosphorylation event in a given sentence and typically has two entities, one that controls the phosphorylation and one that is phosphorylated. events may also involve other events, such as the inhibition of an expression, and may ultimately form partial or entire biological pathways @xcite . state-of-the-art methods for bee rely on learning textual patterns and features from annotated documents where entities and their specific role in an event structure are manually marked. they typically consist of multiple classifiers to solve the different subtasks of trigger, role, and event detection, each requiring individual training and validation data. in this paper, we instead model bee as iterative question answering, using the same model for each of the individual steps which allows knowledge sharing and joint learning of the different event components. we show that this model is as effective in predicting event structures in two bionlp shared tasks @xcite as a baseline consisting of multiple, cnn based classifiers @xcite . the paper is structured as followed: in section 2, we give a brief overview over related work in biomedical event extraction and in question answering. we define the event extraction task, our question answering model, and our evaluation setup in section 3. in section 4, we present our results and discuss them before we conclude the paper. the code and pretrained models are freely available at https://github.com/wangxii/bio_ event_qa . ## related work approaches to bee can be divided into two categories: approaches using manually defined rules @xcite and approaches making use of machine learning algorithms. early approaches of the latter category, such as event-mine @xcite or the turku event extraction system (tees) @xcite , had in common that they achieve event extraction through a pipeline of several independent classifiers, each solving a different subtask of event extraction and each based on a set of specifically defined features extracted from the text, often after heavy and error-prone preprocessing (e.g., pos tagging, dependency parsing). more recent works use neural architectures, where the previously manually defined features are replaced by automatically learned text representations @xcite , involving techniques like word embeddings and other language models. while the original tees (tees svm) @xcite , was based on a pipeline of svms using manually defined features, the more recent tees cnn @xcite additionally incorporates biomedical word embeddings as features and replaces the svms with cnns. as pipelined models suffer from error propagation (for instance, an undetected event trigger in the first phase leads to missing the event entirely), approaches based on joint inference recently became more popular. zhu and zheng (2020) assign a separate probability to each event trigger, relation and event candidate and move the final decision about the veracity of an event structure to an optimization scheme solved in a post-processing step. deepeventmine @xcite is a derivative of eventmine @xcite and makes use of text representations learned by bert @xcite . it tries to avoid error propagation by training a multilayer network for bee in an end-to-end manner and achieves new state-of-the-art performance in various biomedical event extraction corpora. in contrast to these previous approaches, our model employs a network with only one single output layer for all event extraction subtasks and it does not need to introduce a new layer for each subtask. in this work, we will model bee as a an iterative question answering (qa) process. this idea was brought up first by @xcite , who showed how to model ten different nlp tasks, among them machine translation, summarization, and sentiment analysis, as question answering tasks over a properly defined context. @xcite proposed a specific question answering framework for event extraction based on the idea of extracting the entities of individual relations using so-called "question turns". in each turn, the question answering procedure asks a question for a new entity from the relation followed by a text passage where a span is marked as the output entity. found entities from previous turns are included in the questions of subsequent turns to allow for more precise subsequent queries. the process is controlled by predefined question templates which determine the sequence of turns depending on the event type. however, this work is not applicable to bee, because it assumes a fixed number of arguments and has no support for nested events (events that serve as arguments for other events), which are two defining characteristics of the bee task. in this paper, we develop a similar framework for the extraction of nested biomedical events. our framework applies scibert @xcite , a domain-specific refinement of bert @xcite , as underlying qa method. bert (and scibert) is a pre-trained transformer model @xcite which relies on an attention mechanism to learn relationships between different parts of a sequential input, which was shown to better capture long-term dependencies than convolutional and recurrent neural networks. the parameters of its final layers can be used as input features to other models, or can be used in a finetuning procedure involving a further, task-specific layer for problems like question answering, sentence similarity quantification, or sentence continuation prediction. ## conclusion we presented an approach for bee in which this task is modeled as multi-turn question answering problem using bert as underlying language model. we show that our model is able to form event structures from the answers of multiple questions. our experiments show promising results on two corpora, especially in a knowledge base population setting. in future work, we aim to improve model performance by adjusting the event merging procedure and by using further or modified question templates. it would also be worthwhile to study the reasons of the performance gains of our model compared to tees in more detail, for instance by replacing the cnn in tees cnn with bert.