Multimodal Question Answering for Unified Information Extraction
Yuxuan Sun Kai Zhang Yu Su
Multimodal information extraction (MIE) aims to extract structured
information from unstructured multimedia content. Due to the diversity of tasks
and settings, most current MIE models are task-specific and data-intensive,
which limits their generalization to real-world scenarios with diverse task
requirements and limited labeled data. To address these issues, we propose a
novel multimodal question answering (MQA) framework to unify three MIE tasks by
reformulating them into a unified span extraction and multi-choice QA pipeline.
Extensive experiments on six datasets show that: 1) Our MQA framework
consistently and significantly improves the performances of various
off-the-shelf large multimodal models (LMM) on MIE tasks, compared to vanilla
prompting. 2) In the zero-shot setting, MQA outperforms previous
state-of-the-art baselines by a large margin. In addition, the effectiveness of
our framework can successfully transfer to the few-shot setting, enhancing LMMs
on a scale of 10B parameters to be competitive or outperform much larger
language models such as ChatGPT and GPT-4. Our MQA framework can serve as a
general principle of utilizing LMMs to better solve MIE and potentially other
downstream multimodal tasks.