BuDDIE: A Diverse Dataset of 1,665 Business Documents for Multi-task Information Extraction

Core Concepts

BuDDIE is a new dataset of 1,665 real-world business documents that supports three key tasks in visually-rich document understanding: document classification, key entity extraction, and visual question answering.

Abstract

The BuDDIE dataset consists of 1,665 publicly available structured business documents from US state government websites. It is unique in that it tackles multiple distinct visually-rich document understanding tasks: document classification, key entity extraction, and visual question answering.
For document classification, the dataset contains 5 distinct document classes such as amendment documents, application/articles, business entity details, certificates/statements, and periodic reports. Annotators achieved high agreement on the document class labels.
The key entity extraction task features a rich ontology of 69 fine-grained entity types across 7 super categories, including business entities, key personnel, file attributes, government officials, and more. The annotations were validated to ensure high quality.
For visual question answering, the dataset includes both span questions that require extracting a key entity, as well as boolean questions that ask if a certain property of an entity is true or false. The questions cover a diverse range of the annotated key entities.
Overall, BuDDIE provides a comprehensive multi-task benchmark for visually-rich document understanding, with the potential to support additional downstream tasks like multi-turn QA and instruction tuning in the future.

Stats

The dataset contains 1,665 business documents from US state government websites.
There are 38,906 annotated key entities across the dataset.

Quotes

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Key Insights Distilled From

BuDDIE

by Ran Zmigrod,... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04003.pdf

Deeper Inquiries

How can the BuDDIE dataset be extended to support multi-turn visual question answering or other interactive document understanding tasks?

To extend the BuDDIE dataset for multi-turn visual question answering or other interactive document understanding tasks, several steps can be taken:

Annotation Expansion: Include annotations for multi-turn dialogues or conversations within the documents. This would involve annotating the flow of questions and answers between a user and the document.

Contextual Information: Incorporate contextual information within the dataset to enable models to maintain context across multiple turns. This could involve adding metadata or additional annotations that provide context for each turn.

Task-specific Annotations: Introduce annotations that capture the dependencies between questions and answers in a multi-turn setting. This could involve marking relationships between questions and answers to guide the model in understanding the dialogue flow.

Model Training: Develop models that are capable of handling multi-turn interactions. This may involve adapting existing models or creating new architectures that can process and respond to multiple questions and answers in a coherent manner.

By implementing these strategies, the BuDDIE dataset can be enhanced to support multi-turn visual question answering and other interactive document understanding tasks effectively.

How do the performance differences between text-only, multi-modal, and large language models on BuDDIE reflect their relative strengths and limitations for real-world document processing?

Text-Only Models: Text-only models like BERT and RoBERTa perform well on tasks that rely primarily on textual information. They excel at understanding the content of the documents but may struggle with tasks that require integrating visual and layout information.

Multi-Modal Models: Multi-modal models like LayoutLM and LayoutLMv3 leverage both text and visual features, making them suitable for tasks that involve processing documents with complex layouts. They can capture spatial relationships and enhance understanding through visual cues.

Large Language Models (LLMs): Large language models such as GPT4 and DocLLM demonstrate superior performance across tasks due to their extensive pre-training and capacity to handle diverse data types. They excel in capturing intricate patterns and nuances in document understanding tasks.

Strengths and Limitations:

Text-Only Models: Strengths include strong performance on text-based tasks, but limitations arise when dealing with visual or layout-dependent tasks.
Multi-Modal Models: Strengths lie in integrating text and visual information, but limitations may include complexity in training and potential challenges in handling diverse document types.
Large Language Models: Strengths encompass high performance across tasks and data types, but limitations may include computational requirements and potential biases in training data.
The performance differences highlight the importance of selecting the right model based on the specific requirements of the document processing task at hand.

What insights about the nature of business documents and their information needs can be gained by analyzing the types of questions asked in the BuDDIE visual question answering task?

Analyzing the types of questions asked in the BuDDIE visual question answering task can provide valuable insights into the nature of business documents and their information needs:

Key Information Extraction: By examining the questions, we can identify the key entities and information that users are seeking within business documents. This can help prioritize the extraction and understanding of critical data points.

Document Structure Understanding: The questions can reveal the structure and layout of business documents that users find challenging or confusing. Understanding these pain points can guide document design improvements.

User Intent and Interactions: Analyzing the questions can shed light on user intent and the specific interactions users have with business documents. This insight can inform the design of more user-friendly and informative documents.

Content Relevance: The types of questions asked can indicate the relevance and importance of different sections or elements within business documents. This can guide content organization and presentation for better user comprehension.

Information Retrieval: Insights from question analysis can enhance information retrieval systems by highlighting the most sought-after information within business documents. This can streamline access to critical data for users.

By delving into the types of questions asked in the BuDDIE visual question answering task, businesses can tailor their document processing strategies to better meet user needs and improve overall document understanding.