Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language
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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 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
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