JDocQA: Japanese Document Question Answering Dataset for Generative Language Models

• URL: http://arxiv.org/abs/2403.19454v1
• Date: Thu, 28 Mar 2024 14:22:54 GMT
• Title: JDocQA: Japanese Document Question Answering Dataset for Generative Language Models
• Authors: Eri Onami, Shuhei Kurita, Taiki Miyanishi, Taro Watanabe,
• Abstract summary: We introduce Japanese Document Question Answering (JDocQA), a large-scale document-based QA dataset. It comprises 5,504 documents in PDF format and annotated 11,600 question-and-answer instances in Japanese. We incorporate multiple categories of questions and unanswerable questions from the document for realistic question-answering applications.
• Score: 15.950718839723027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Document question answering is a task of question answering on given documents such as reports, slides, pamphlets, and websites, and it is a truly demanding task as paper and electronic forms of documents are so common in our society. This is known as a quite challenging task because it requires not only text understanding but also understanding of figures and tables, and hence visual question answering (VQA) methods are often examined in addition to textual approaches. We introduce Japanese Document Question Answering (JDocQA), a large-scale document-based QA dataset, essentially requiring both visual and textual information to answer questions, which comprises 5,504 documents in PDF format and annotated 11,600 question-and-answer instances in Japanese. Each QA instance includes references to the document pages and bounding boxes for the answer clues. We incorporate multiple categories of questions and unanswerable questions from the document for realistic question-answering applications. We empirically evaluate the effectiveness of our dataset with text-based large language models (LLMs) and multimodal models. Incorporating unanswerable questions in finetuning may contribute to harnessing the so-called hallucination generation.

Related papers

• PCoQA: Persian Conversational Question Answering Dataset [12.07607688189035]
  The PCoQA dataset is a resource comprising information-seeking dialogs encompassing a total of 9,026 contextually-driven questions. PCoQA is designed to present novel challenges compared to previous question answering datasets. This paper not only presents the comprehensive PCoQA dataset but also reports the performance of various benchmark models.
  arXiv  Detail & Related papers  (2023-12-07T15:29:34Z)
• DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding [19.713647367008143]
  We introduce a novel task named document-level chart question answering (DCQA) The newly developed benchmark dataset comprises 50,010 synthetic documents integrating charts in a wide range of styles. We present the development of a potent question-answer generation engine that employs table data, a rich color set, and basic question templates.
  arXiv  Detail & Related papers  (2023-10-29T11:38:08Z)
• PDFTriage: Question Answering over Long, Structured Documents [60.96667912964659]
  Representing structured documents as plain text is incongruous with the user's mental model of these documents with rich structure. We propose PDFTriage that enables models to retrieve the context based on either structure or content. Our benchmark dataset consists of 900+ human-generated questions over 80 structured documents.
  arXiv  Detail & Related papers  (2023-09-16T04:29:05Z)
• Towards Complex Document Understanding By Discrete Reasoning [77.91722463958743]
  Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language. We introduce a new Document VQA dataset, named TAT-DQA, which consists of 3,067 document pages and 16,558 question-answer pairs. We develop a novel model named MHST that takes into account the information in multi-modalities, including text, layout and visual image, to intelligently address different types of questions.
  arXiv  Detail & Related papers  (2022-07-25T01:43:19Z)
• V-Doc : Visual questions answers with Documents [1.6785823565413143]
  V-Doc is a question-answering tool using document images and PDF. It supports generating and using both extractive and abstractive question-answer pairs.
  arXiv  Detail & Related papers  (2022-05-27T02:38:09Z)
• A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers [66.11048565324468]
  We present a dataset of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers.
  arXiv  Detail & Related papers  (2021-05-07T00:12:34Z)
• MultiModalQA: Complex Question Answering over Text, Tables and Images [52.25399438133274]
  We present MultiModalQA: a dataset that requires joint reasoning over text, tables and images. We create MMQA using a new framework for generating complex multi-modal questions at scale. We then define a formal language that allows us to take questions that can be answered from a single modality, and combine them to generate cross-modal questions.
  arXiv  Detail & Related papers  (2021-04-13T09:14:28Z)
• XOR QA: Cross-lingual Open-Retrieval Question Answering [75.20578121267411]
  This work extends open-retrieval question answering to a cross-lingual setting. We construct a large-scale dataset built on questions lacking same-language answers.
  arXiv  Detail & Related papers  (2020-10-22T16:47:17Z)
• Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
  We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
  arXiv  Detail & Related papers  (2020-10-04T19:03:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.