OpenCQA: Open-ended Question Answering with Charts

• URL: http://arxiv.org/abs/2210.06628v1
• Date: Wed, 12 Oct 2022 23:37:30 GMT
• Title: OpenCQA: Open-ended Question Answering with Charts
• Authors: Shankar Kantharaj, Xuan Long Do, Rixie Tiffany Ko Leong, Jia Qing Tan, Enamul Hoque, Shafiq Joty
• Abstract summary: We introduce a new task called OpenCQA, where the goal is to answer an open-ended question about a chart with texts. We implement and evaluate a set of baselines under three practical settings. Our analysis of the results show that the top performing models generally produce fluent and coherent text.
• Score: 6.7038829115674945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Charts are very popular to analyze data and convey important insights. People often analyze visualizations to answer open-ended questions that require explanatory answers. Answering such questions are often difficult and time-consuming as it requires a lot of cognitive and perceptual efforts. To address this challenge, we introduce a new task called OpenCQA, where the goal is to answer an open-ended question about a chart with descriptive texts. We present the annotation process and an in-depth analysis of our dataset. We implement and evaluate a set of baselines under three practical settings. In the first setting, a chart and the accompanying article is provided as input to the model. The second setting provides only the relevant paragraph(s) to the chart instead of the entire article, whereas the third setting requires the model to generate an answer solely based on the chart. Our analysis of the results show that the top performing models generally produce fluent and coherent text while they struggle to perform complex logical and arithmetic reasoning.

Related papers

• CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs [62.84082370758761]
  CharXiv is a comprehensive evaluation suite involving 2,323 charts from arXiv papers. To ensure quality, all charts and questions are handpicked, curated, and verified by human experts. Results reveal a substantial, previously underestimated gap between the reasoning skills of the strongest proprietary model.
  arXiv  Detail & Related papers  (2024-06-26T17:50:11Z)
• Enhancing Question Answering on Charts Through Effective Pre-training Tasks [26.571522748519584]
  We address the limitation of current VisualQA models when applied to charts and plots. Our findings indicate that existing models particularly underperform in answering questions related to the chart's structural and visual context. We propose three simple pre-training tasks that enforce the existing model in terms of both structural-visual knowledge, as well as its understanding of numerical questions.
  arXiv  Detail & Related papers  (2024-06-14T14:40:10Z)
• QAGCF: Graph Collaborative Filtering for Q&A Recommendation [58.21387109664593]
  Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs. This makes user behaviors more complex, and presents two challenges for Q&A recommendation. We introduce Question & Answer Graph Collaborative Filtering (QAGCF), a graph neural network model that creates separate graphs for collaborative and semantic views.
  arXiv  Detail & Related papers  (2024-06-07T10:52:37Z)
• 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)
• 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)
• Chart Question Answering: State of the Art and Future Directions [0.0]
  Chart Question Answering (CQA) systems typically take a chart and a natural language question as input and automatically generate the answer. We systematically review the current state-of-the-art research focusing on the problem of chart question answering.
  arXiv  Detail & Related papers  (2022-05-08T22:54:28Z)
• ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning [7.192233658525916]
  We present a benchmark covering 9.6K human-written questions and 23.1K questions generated from human-written chart summaries. We present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions.
  arXiv  Detail & Related papers  (2022-03-19T05:00:30Z)
• Question-Answer Sentence Graph for Joint Modeling Answer Selection [122.29142965960138]
  We train and integrate state-of-the-art (SOTA) models for computing scores between question-question, question-answer, and answer-answer pairs. Online inference is then performed to solve the AS2 task on unseen queries.
  arXiv  Detail & Related papers  (2022-02-16T05:59:53Z)
• Classification-Regression for Chart Comprehension [16.311371103939205]
  Chart question answering (CQA) is a task used for assessing chart comprehension. We propose a new model that jointly learns classification and regression. Our model's edge is particularly emphasized on questions with out-of-vocabulary answers.
  arXiv  Detail & Related papers  (2021-11-29T18:46:06Z)
• AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization [73.91543616777064]
  Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. One goal of answer summarization is to produce a summary that reflects the range of answer perspectives. This work introduces a novel dataset of 4,631 CQA threads for answer summarization, curated by professional linguists.
  arXiv  Detail & Related papers  (2021-11-11T21:48:02Z)
• Graph-Based Tri-Attention Network for Answer Ranking in CQA [56.42018099917321]
  We propose a novel graph-based tri-attention network, namely GTAN, to generate answer ranking scores. Experiments on three real-world CQA datasets demonstrate GTAN significantly outperforms state-of-the-art answer ranking methods.
  arXiv  Detail & Related papers  (2021-03-05T10:40:38Z)

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.