Global Constraints with Prompting for Zero-Shot Event Argument Classification (2023)

Abstract: Determining the role of event arguments is a crucial subtask of event extraction. Most previous supervised models leverage costly annotations, which is not practical for open-domain applications. In this work, we propose to use global constraints with prompting to effectively tackles event argument classification without any annotation and task-specific training. Specifically, given an event and its associated passage, the model first creates several new passages by prefix prompts and cloze prompts, where prefix prompts indicate event type and trigger span, and cloze prompts connect each candidate role with the target argument span. Then, a pre-trained language model scores the new passages, making the initial prediction. Our novel prompt templates can easily adapt to all events and argument types without manual effort. Next, the model regularizes the prediction by global constraints exploiting cross-task, cross-argument, and cross-event relations. Extensive experiments demonstrate our model’s effectiveness: it outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1, respectively, without given argument spans. We have made our code publicly available.

## introduction event argument classification 2 (eac), finding the roles of event arguments, is an important and challenging event extraction sub-task. as shown in figure 1 , a "transfer-money" event whose trigger is "acquiring" has several argument spans (e.g., "daily planet"). by determining the role of these arguments (e.g., "daily planet" as "beneficiary"), we 1 https://github.com/hkust-knowcomp/constraintswith-prompting-for-zero-shot-eac 2 we focus on event argument because existing zero-shot trigger extraction models like @xcite are already strong enough, but the arguments remain a challenge. our argument identification approach is described in section 3.1. can obtain a better understanding of the event, thus benefiting related applications like stock price prediction @xcite and biomedical research @xcite . many previous eac works require numerous annotations to train their models @xcite @xcite , which is not only costly as the annotations are labor-intensive but also difficult to be generalized to datasets of novel domains. accordingly, some eac models adopt a few-shot learning paradigm @xcite . however, they are sensitive to the fewshot example selection and they still require costly task-specific training, which hinders their real-life deployment. there have been some zero-shot eac models based on transfer learning @xcite , or label semantics @xcite , or prompt learning @xcite @xcite . however, these models' corresponding limitations impede their real-life deployment. the model based on transfer learning can be ineffective when new event types are very different from the observed one. as for models using label semantics, they require a laborious preparation process and have unsatisfactory performance. regarding models adopting prompt learning, they need tedious prompt design customized to every new type of events and arguments, and their performance is also limited. to address the aforementioned issues, we propose an approach using global constraints with prompting to tackle zero-shot eac. global constraints can be viewed as a type of supervision signal from domain knowledge, which is crucial for zero-shot eac since supervision from annotations is inaccessible. moreover, our model's constraints module provides abundant global insights across tasks, arguments, and events. prompting can also be regarded as a supervision signal as it induces abundant knowledge from pre-trained language models (ptlm). unlike previous zero-shot eac works, which need a tedious prompt design for every new type of events and arguments, the novel prompt templates of our model's prompting module can be easily adapted to all possible types of events and arguments in a fully automatic way. specifically, given an event and its passage, our model first adds prefix prompt, cloze prompt, and candidate roles into the passage, which creates a set of new passages. the prefix prompt describes the event type and trigger span. cloze prompt connects each candidate to the target argument span. afterwards, our model adopts a ptlm to compute the language modeling loss for each of the new passages, whose negative value would be the respective prompting score. the role with the highest prompting score is the initial prediction. then, our model uses global constraints to regularize the initial prediction. the global constraints are based on the domain knowledge of the following relations: (1) cross-task relation, where our model additionally performs another one or more classification task on target argument span, and our model's predictions on eac and other task(s) should be consistent; (2) cross-argument relation, where arguments of one event should collectively abide by certain constraint(s); (3) cross-event relation, where some argument playing a certain role in one event should play a typical role in another related event. we conduct comprehensive experiments to demonstrate the effectiveness of our model. particularly, our approach surpasses all zero-shot baselines by at least 12.5% and 10.9% f1 on ace and ere, respectively. when argument spans are not given, our model outperforms the best zero-shot baseline by 4.3% and 3.3% f1 on ace and ere, respectively. besides that, we also conduct experiments to show that both the prompting and constraints modules contribute to the final success. ## methodology we first present an overview of our approach. then we introduce the details by describing its prompting module and global constraints regularization module. we follow @xcite to name a prompt inserted before input text as prefix prompt, and a prompt with slot(s) to fill in and insert in the middle of input text as cloze prompt. ## experiments we first present the experimental settings, baselines used for comparison, and some implementation details. next, we show and analyze the experiment results. then we present a detailed analysis of the prompting module and global constraints regularization module. finally, we conduct an error analysis. ## related work in this section, we introduce related works about constraint modeling, event extractions, and promptbased information extraction (ie). ## conclusion we propose a zero-shot eac model using global constraints with prompting. compared with previ-