CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart

• URL: http://arxiv.org/abs/2410.21414v1
• Date: Mon, 28 Oct 2024 18:13:14 GMT
• Title: CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart
• Authors: Bowen Zhao, Tianhao Cheng, Yuejie Zhang, Ying Cheng, Rui Feng, Xiaobo Zhang,
• Abstract summary: We present C$textT2$C-QA, a pioneering Chinese reasoning-based QA dataset that includes an extensive collection of text, tables, and charts. Our dataset simulates real webpages and serves as a great test for the capability of the model to analyze and reason with multimodal data.
• Score: 26.54501344351476
• License:
• Abstract: Multimodal Question Answering (MMQA) is crucial as it enables comprehensive understanding and accurate responses by integrating insights from diverse data representations such as tables, charts, and text. Most existing researches in MMQA only focus on two modalities such as image-text QA, table-text QA and chart-text QA, and there remains a notable scarcity in studies that investigate the joint analysis of text, tables, and charts. In this paper, we present C$\text{T}^2$C-QA, a pioneering Chinese reasoning-based QA dataset that includes an extensive collection of text, tables, and charts, meticulously compiled from 200 selectively sourced webpages. Our dataset simulates real webpages and serves as a great test for the capability of the model to analyze and reason with multimodal data, because the answer to a question could appear in various modalities, or even potentially not exist at all. Additionally, we present AED (\textbf{A}llocating, \textbf{E}xpert and \textbf{D}esicion), a multi-agent system implemented through collaborative deployment, information interaction, and collective decision-making among different agents. Specifically, the Assignment Agent is in charge of selecting and activating expert agents, including those proficient in text, tables, and charts. The Decision Agent bears the responsibility of delivering the final verdict, drawing upon the analytical insights provided by these expert agents. We execute a comprehensive analysis, comparing AED with various state-of-the-art models in MMQA, including GPT-4. The experimental outcomes demonstrate that current methodologies, including GPT-4, are yet to meet the benchmarks set by our dataset.

Related papers

• Knowledge-Aware Reasoning over Multimodal Semi-structured Tables [85.24395216111462]
  This study investigates whether current AI models can perform knowledge-aware reasoning on multimodal structured data. We introduce MMTabQA, a new dataset designed for this purpose. Our experiments highlight substantial challenges for current AI models in effectively integrating and interpreting multiple text and image inputs.
  arXiv  Detail & Related papers  (2024-08-25T15:17:43Z)
• SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers [43.18330795060871]
  SPIQA is a dataset specifically designed to interpret complex figures and tables within the context of scientific research articles. We employ automatic and manual curation to create the dataset. SPIQA comprises 270K questions divided into training, validation, and three different evaluation splits.
  arXiv  Detail & Related papers  (2024-07-12T16:37:59Z)
• TANQ: An open domain dataset of table answered questions [15.323690523538572]
  TANQ is the first open domain question answering dataset where the answers require building tables from information across multiple sources. We release the full source attribution for every cell in the resulting table and benchmark state-of-the-art language models in open, oracle, and closed book setups. Our best-performing baseline, GPT4 reaches an overall F1 score of 29.1, lagging behind human performance by 19.7 points.
  arXiv  Detail & Related papers  (2024-05-13T14:07:20Z)
• SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph [16.275155481031348]
  We propose a Situated Conversation Agent Petrained with Multimodal Questions from INcremental Layout Graph (SPRING) All QA pairs utilized during pretraining are generated from novel Incremental Layout Graphs (ILG) Experimental results verify the SPRING's effectiveness, showing that it significantly outperforms state-of-the-art approaches on both SIMMC 1.0 and SIMMC 2.0 datasets.
  arXiv  Detail & Related papers  (2023-01-05T08:03:47Z)
• Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA [85.17249272519626]
  An optimized OpenQA Table-Text Retriever (OTTeR) is proposed. We conduct retrieval-centric mixed-modality synthetic pre-training. OTTeR substantially improves the performance of table-and-text retrieval on the OTT-QA dataset.
  arXiv  Detail & Related papers  (2022-10-11T07:04:39Z)
• 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)
• HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling [50.39787601462344]
  Community Question Answering (CQA) is a well-defined task that can be used in many scenarios, such as E-Commerce and online user community for special interests. Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question. We propose a question-aware heterogeneous graph transformer to incorporate the multiple information sources (MIS) in the user community to automatically generate the answer.
  arXiv  Detail & Related papers  (2021-12-27T10:16:43Z)
• MetaQA: Combining Expert Agents for Multi-Skill Question Answering [49.35261724460689]
  We argue that despite the promising results of multi-dataset models, some domains or QA formats might require specific architectures. We propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores.
  arXiv  Detail & Related papers  (2021-12-03T14:05:52Z)
• TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance [71.76018597965378]
  We build a new large-scale Question Answering dataset containing both Tabular And Textual data, named TAT-QA. We propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text.
  arXiv  Detail & Related papers  (2021-05-17T06:12:06Z)

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.