Japanese Document Question Answering (JDocQA) Dataset for Generative Language Models

Core Concepts

JDocQA is a large-scale Japanese document question answering dataset that requires understanding of both textual and visual information to answer questions.

Abstract

The JDocQA dataset was created by collecting 5,504 Japanese documents in various formats (pamphlets, slides, reports, websites) and annotating 11,600 question-answer pairs on them. The questions cover four categories: yes/no, factoid, numerical, and open-ended. The dataset also includes 1,000 unanswerable questions where the correct answer is not mentioned in the given documents. The key highlights of the dataset are: Multimodal nature: Questions require understanding of both textual and visual elements (figures, tables, charts) in the documents. Diverse question types: The dataset includes yes/no, factoid, numerical, and open-ended questions. Unanswerable questions: 1,000 questions have no answer in the given documents, testing the model's ability to detect unanswerable cases. Multilingual: The dataset is in Japanese, addressing the lack of non-English document question answering resources. The authors conducted experiments with both text-only and multimodal models, evaluating their performance on the JDocQA dataset. They found that incorporating unanswerable questions during finetuning can help mitigate hallucination in language model outputs.

Stats

The dataset contains 5,504 documents and 11,600 question-answer pairs. The documents include 1,715 pamphlets, 1,640 slides, 2,086 reports, and 67 websites. The question types are: 1,855 yes/no, 2,052 factoid, 1,866 numerical, and 5,827 open-ended. 1,788 questions require referencing multiple pages, and 1,000 questions are unanswerable.

Quotes

"Incorporating unanswerable questions in finetuning may contribute to harnessing the so-called hallucination generation." "JDocQA consists of 11,600 question and answer pairs on the collected 5,504 documents as references for answering the question, four different question categories and 1,000 multi-page questions."

Key Insights Distilled From

JDocQA

by Eri Onami,Sh... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19454.pdf

Deeper Inquiries

How can the JDocQA dataset be extended to cover a wider range of document types and topics beyond the current focus on government and public sector materials?

To extend the JDocQA dataset to cover a wider range of document types and topics, several steps can be taken: Diversifying Document Sources: Include documents from various sectors such as healthcare, finance, education, and technology to provide a more comprehensive dataset. Incorporating Different Document Formats: Add documents in different formats like research papers, technical manuals, legal documents, and news articles to capture a broader spectrum of document types. Expanding Language Support: Consider including documents in multiple languages to cater to a more diverse user base and enhance the dataset's applicability. Introducing Specialized Topics: Incorporate documents on specialized topics like scientific research, engineering, environmental studies, and more to address a wider range of subject areas.

What are the potential challenges in applying the JDocQA dataset to real-world document processing tasks, and how can the dataset be further improved to address those challenges?

Challenges in applying the JDocQA dataset to real-world document processing tasks may include: Domain Specificity: Documents in real-world scenarios may cover highly specialized topics not present in the dataset, requiring domain adaptation techniques. Ambiguity and Context Understanding: Real-world documents often contain nuanced information and require a deep understanding of context, posing challenges for question answering models. Scalability: Handling a large volume of diverse documents efficiently can be a challenge, necessitating scalable processing methods. To address these challenges, the dataset can be improved by: Increasing Diversity: Continuously adding new document types and topics to enhance the dataset's coverage and relevance to real-world applications. Fine-tuning Models: Fine-tuning models on a wider range of documents and topics to improve their performance on diverse content. Incorporating Feedback Mechanisms: Implementing mechanisms for users to provide feedback on model responses to iteratively improve performance.

Given the multimodal nature of the dataset, how can the integration of visual and textual information be better leveraged to improve question answering performance, beyond the current approaches explored in the paper?

To leverage the integration of visual and textual information in the JDocQA dataset for improved question answering performance, the following strategies can be considered: Cross-Modal Attention Mechanisms: Implementing advanced cross-modal attention mechanisms to effectively capture relationships between textual and visual elements in documents. Semantic Fusion Techniques: Utilizing semantic fusion techniques to combine information from both modalities for a more comprehensive understanding of the content. Contextual Embeddings: Generating contextual embeddings that capture the interactions between text and images to enhance the model's understanding of document content. Interactive Learning: Implementing interactive learning approaches where the model can dynamically adjust its focus between text and visual inputs based on the context of the question. Transfer Learning: Leveraging transfer learning techniques to pretrain models on a diverse range of multimodal data to improve their ability to handle various document types and topics effectively.