ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT
Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen, Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, Yong Jiang, and, Wenjuan Han

TL;DR
ChatIE leverages ChatGPT in a two-stage prompt-based framework to perform zero-shot information extraction across multiple tasks and languages, achieving competitive results without annotated data.
Contribution
This work introduces a novel multi-turn question-answering framework using ChatGPT for zero-shot IE, demonstrating its effectiveness across various tasks and datasets.
Findings
ChatIE surpasses some fully supervised models on several datasets.
It achieves high performance on entity-relation, NER, and event extraction tasks.
The approach works well across multiple languages and datasets.
Abstract
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text. It is challenging due to involving little human intervention. Challenging but worthwhile, zero-shot IE reduces the time and effort that data labeling takes. Recent efforts on large language models (LLMs, e.g., GPT-3, ChatGPT) show promising performance on zero-shot settings, thus inspiring us to explore prompt-based methods. In this work, we ask whether strong IE models can be constructed by directly prompting LLMs. Specifically, we transform the zero-shot IE task into a multi-turn question-answering problem with a two-stage framework (ChatIE). With the power of ChatGPT, we extensively evaluate our framework on three IE tasks: entity-relation triple extract, named entity recognition, and event extraction. Empirical results on six datasets across two languages show that ChatIE achieves impressive…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Data Quality and Management
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Multi-Head Attention · Cosine Annealing · Linear Layer · Dense Connections · Weight Decay · Linear Warmup With Cosine Annealing · Adam · Dropout · Byte Pair Encoding
