no code implementations • 14 Mar 2024 • Ahmed Masry, Mehrad Shahmohammadi, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty
Further evaluation shows that our instruction-tuning approach supports a wide array of real-world chart comprehension and reasoning scenarios, thereby expanding the scope and applicability of our models to new kinds of tasks.
no code implementations • 26 Jan 2024 • Ahmed Masry, Amir Hajian
We also propose the LongForms dataset, a comprehensive financial dataset that encapsulates several industrial challenges in financial documents.
no code implementations • 17 Dec 2023 • Xuan Long Do, Mohammad Hassanpour, Ahmed Masry, Parsa Kavehzadeh, Enamul Hoque, Shafiq Joty
However, their application to chart-related tasks is not trivial as these tasks typically involve considering not only the underlying data but also the visual features in the chart image.
1 code implementation • 24 May 2023 • Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, Shafiq Joty
Charts are very popular for analyzing data, visualizing key insights and answering complex reasoning questions about data.
Ranked #15 on Chart Question Answering on ChartQA (using extra training data)
no code implementations • 8 May 2022 • Enamul Hoque, Parsa Kavehzadeh, Ahmed Masry
Information visualizations such as bar charts and line charts are very common for analyzing data and discovering critical insights.
1 code implementation • Findings (ACL) 2022 • Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, Enamul Hoque
To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions.
Ranked #1 on Chart Question Answering on RealCQA
2 code implementations • ACL 2022 • Shankar Kantharaj, Rixie Tiffany Ko Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty
We also introduce a number of state-of-the-art neural models as baselines that utilize image captioning and data-to-text generation techniques to tackle two problem variations: one assumes the underlying data table of the chart is available while the other needs to extract data from chart images.