Open Ended Question Answering with Charts

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dc.contributor.advisorPrince, Enamul Hoque
dc.contributor.authorKantharaj, Shankar Santhosh
dc.date.accessioned2022-12-14T16:43:29Z
dc.date.available2022-12-14T16:43:29Z
dc.date.copyright2022-10-04
dc.date.issued2022-12-14
dc.date.updated2022-12-14T16:43:29Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractCharts 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. 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.
dc.identifier.urihttp://hdl.handle.net/10315/40786
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectArtificial intelligence
dc.subjectComputer science
dc.subject.keywordsOpenCQA
dc.subject.keywordsMachine learning
dc.subject.keywordsDeep learning
dc.subject.keywordsNLP
dc.subject.keywordsNLG
dc.subject.keywordsCharts
dc.titleOpen Ended Question Answering with Charts
dc.typeElectronic Thesis or Dissertation

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