Event Extraction as Multi-turn Question Answering (2020)

Abstract: Event extraction, which aims to identify event triggers of pre-defined event types and their arguments of specific roles, is a challenging task in NLP. Most traditional approaches formulate this task as classification problems, with event types or argument roles taken as golden labels. Such approaches fail to model rich interactions among event types and arguments of different roles, and cannot generalize to new types or roles. This work proposes a new paradigm that formulates event extraction as multi-turn question answering. Our approach, MQAEE, casts the extraction task into a series of reading comprehension problems, by which it extracts triggers and arguments successively from a given sentence. A history answer embedding strategy is further adopted to model question answering history in the multi-turn process. By this new formulation, MQAEE makes full use of dependency among arguments and event types, and generalizes well to new types with new argument roles. Empirical results on ACE 2005 shows that MQAEE outperforms current state-of-the-art, pushing the final F1 of argument extraction to 53.4% (+2.0%). And it also has a good generalization ability, achieving competitive performance on 13 new event types even if trained only with a few samples of them.

## introduction event extraction is an important yet challenging task in natural language understanding. given a sentence, an event extraction system ought to identify event triggers with specific event types, as well as their corresponding arguments with specific argument roles. as an example, figure 1 presents an event mention of the type movement transport, triggered by "left". there are three arguments: "saddam's family" playing the role of artifact, "that city" the role of origin, and "three days ago" the role of time-within. typically, event extraction can be divided into two subtasks: trigger extraction (trigger identification and classification) and argument extraction (argument identification and classification), as defined by the standard automatic content extraction (ace) 2005 benchmark @xcite . current approaches to event extraction can thus be roughly categorized into two groups: (1) pipelined approaches that perform trigger extraction and argument extraction in separate stages @xcite @xcite @xcite ; (2) joint approaches that perform all subtasks simultaneously in a joint learning fashion @xcite @xcite . most of these approaches, whether pipelined or joint, formulate event extraction as classification tasks, by classifying event triggers into pre-defined event types (trigger extraction), and further event arguments into pre-defined argument roles (argument extraction). by treating event types and argument roles directly as golden labels, such classificationbased approaches suffer from two limitations. first of all, they cannot explicitly model the semantics of these golden labels and also fail to capture the rich interactions among them, which could be extremely useful for event extraction. consider the example in figure 1 . the event type movement transport actually provides valuable supplements to the corresponding argument roles like origin and time-within. besides, given that "saddam's family" (the subject of the sentence) is an argument of the role artifact, it is more likely to infer "that city" (the object) might be an argument of the role origin. effectively modeling these semantics and interactions would definitely be beneficial. the second limitation lies in the generalization ability. by taking event types and argument roles as golden labels, classification-based approaches are not able to be generalized to new event types or argument roles without additional annotations. huang et al. (2018) the key idea of their approach is to represent event mentions and event types (or arguments and argument roles) in a shared semantic space, and cast trigger (or argument) classification as a semantic matching problem. this approach generalizes better to new event types and argument roles. but it can hardly capture full mention-type (or argumentrole) interactions simply with the final cosine similarity matching. and it also relies heavily on structured features such as trees or paths derived by the amr parser @xcite , prone to error propagation. to address the above limitations, we propose a new paradigm that formulates event extraction as multi-turn question answering (qa). our approach, referred to as mqaee, splits event extraction into three sub-tasks: trigger identification, trigger classification, and argument extraction. these subtasks are modeled by a series of machine reading comprehension (mrc) based qa templates. trigger identification is cast into an extractive mrc problem, identifying trigger words from given sentences. trigger classification is formalized as a yes/no qa problem, judging whether or not a candidate trigger belongs to a specific event type. argument extraction is also solved via extractive mrc, with questions constructed iteratively by a target event type and the corresponding argument roles. passage: saddam's family left that city three days ago. we evaluate our approach on the standard ace 2005 benchmark. experimental results show that mqaee significantly outperforms current stateof-the-art, pushing the final f1 score of argument extraction to 53.4% (+2.0%). moreover, mqaee generalizes well to new event types, achieving competitive results on the 13 new event types even if trained only with the a few samples of them. our contributions are summarized as follows: (1) we propose a novel multi-turn qa framework for event extraction, which makes full use of rich interactions among triggers, event types, and arguments, and generalizes well to new event types. ## preliminaries this section formally defines the event extraction task, and then introduces mrc techniques based on pre-trained language models @xcite . ## our approach mqaee splits the event extraction task into three sub-tasks: trigger identification, trigger classification, and argument extraction, solved as multi-turn qa in a pipelined fashion. the first turn is devoted to trigger identification, formalized as an extractive mrc problem that recognizes trigger words from a given sentence. the second turn is then designed for trigger classification, formalized as a yes/no problem so as to judge whether a recognized trigger belongs to a specific event type. the rest of the qa process deals with argument extraction, which is formalized as successive extractive mrc problems, identifying arguments of specific roles associated with a predicted event type one by one. see ## experiments this section presents our experiments on the ace 2005 benchmark, foot_2 demonstrating the effectiveness and generalization ability of our approach. ## conclusion and future work this work presents a novel multi-turn qa paradigm for event extraction, referred to as mqaee. it splits event extraction into three sub-tasks: trigger identification, trigger classification, and argument extraction, solved as a series of reading comprehension problems in a pipelined fashion. within the multiturn process, a history answer embedding strategy is further introduced to effectively model qa history. by this new formulation, mqaee makes full use of dependency among arguments and event types, and generalizes well to new types with new argument roles. experimental results on ace 2005 demonstrate the effectiveness and generalization ability of our approach. as future work, we would like to apply reinforcement learning to determine a better qa order for argument extraction in the multi-turn framework, and explore more variants of the model architecture.