Large Language Model (LLM) trained on the mixture of text and code has demonstrated impressive capability in translating natural language (NL) into structured code. In this work, we propose Code4Struct to leverage such text-to-structure translation capability to tackle structured prediction tasks in NLP. For example, Event Argument Extraction (EAE) aims to convert text into event-argument structures that can be represented as a class object using code. This alignment between structures and code enables us to take advantage of Programming Language (PL) features such as inheritance and type annotation to introduce external knowledge or add constraints with ease. We exploit the analogy between PL and NLP problems, and, as a case study, we use Code4Struct to tackle the EAE task using code generation. We ask a LLM to generate code to instantiate an event class with predicted arguments given a NL sentence. Despite only using 50 training instances for each event type, Code4Struct is comparable to fully-supervised models trained on 4,202 event instances and, when given the same 50-shot data, outperforms current state-of-the-art (SOTA) by 20.8% absolute F1. When prompted with hierarchical event types implemented using inheritance, Code4Struct can predict arguments for low-resource event types using 10-shot training instances from its sibling event type and outperforms zero-shot baseline by 12% absolute F1.

Code4Struct利用LLM的text-to-structure翻译能力解决了NLP中的结构预测任务。将PL与NLP问题类比，Code4Struct通过生成代码处理EAE任务，仅使用50个训练实例即可超越目前最先进的模型，在继承中实现层次事件类型时，仅使用10个shot训练实例即可优于零-shot基线。

Code4Struct: 自然语言生成少样本结构化预测代码