# An Event Extraction Approach Based on a Multi-Round Q&A Framework
###### Abstract
However, such methods require a large amount of labeled data to train the model for the extraction task, which not only requires expensive labeled sample cost but also limits the diversity of events covered, and their reliance on upstream tasks inevitably generates the problem of error accumulation. In response to the above problems, some researchers have proposed the use of machine reading comprehension model for event extraction in the last two years. Such methods transform the event extraction task into a reading comprehension problem, which can take advantage of the pre-trained models on the one hand, and introduce a priori information into the problem on the other hand, further alleviating the dependence on the upstream task and the problem of incomplete retention of semantic information. Although the approaches based on the machine reading comprehension model effectively enhance event extraction in low-resource contexts, the existing methods have two shortcomings that need further improvement. First, existing machine reading comprehension methods model the Q&A task in a single-round Q&A model, ignoring the valid information generated in the previous round of Q&A, while a multi-round Q&A process can effectively retain and utilize the a priori information learned in each round of Q&A pairs to gradually obtain the entities required in the next round, further achieving the effect of enhancing semantic information. Second, the existing model extracts independently for each element of the thesis element in the event, ignoring the dependency relation between the thesis elements, and introducing the information of historical answers can effectively tap into the correlation between different thesis elements and improve the performance of the model. Consider the following example sentence. **Example 2**. : _May 4 (Xinhua), British anti-terror police arrested [PERSON] in London on May 2._In the sentence of Example 2, if a single-round question and answer are used, the lack of semantic information at the multi-round level will lead to ambiguity in entity recognition when extracting temporal argument information for \"May 4\" and \"May 2\". However, if \"British anti-terror police\" as the arresting party (Arrest-Jail, Agent) and \"[PERSON]\" as the arrested person (Arrest-Jail, Person) in the event were extracted during the previous rounds of questions and answers, they could be integrated into the subsequent rounds of questions, which could be disambiguated across sentences and effectively extract the time element \"May 2\" (Arrest-Jail, Time) in the event of arrest and imprisonment accurately. Therefore, building a multi-round Q&A framework helps the performance of the event extraction task. To address the above shortcomings, this paper investigates how to introduce valid information into the Q&A framework to further enhance the performance of the event extraction model based on the existing machine reading comprehension model. Specifically, this paper extends and refines the model input and model framework, respectively, and proposes an event extraction method based on a multi-round Q&A model. The main contributions of this paper include the following three points:  
(1) In terms of model input, this paper constructs a multi-round problem template for event argument elements extraction, which enables the model to effectively use prior knowledge to learn more adequate semantic information for additional performance gains. (2) In terms of the model framework, this paper introduces historical answer information into the multi-round question-and-answer framework, which is populated into the corresponding slots of the question rounds, enabling the model to effectively capture the correlations and hierarchical dependencies among the argument entities. (3) A multi-round Q&A model based on a machine reading comprehension model for event extraction is proposed in combination with the above two aspects, and its effectiveness is verified on a public dataset.