@inproceedings{kantharaj-etal-2022-opencqa,
title = "{O}pen{CQA}: Open-ended Question Answering with Charts",
author = "Kantharaj, Shankar and
Do, Xuan Long and
Leong, Rixie Tiffany and
Tan, Jia Qing and
Hoque, Enamul and
Joty, Shafiq",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.811",
pages = "11817--11837",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T OpenCQA: Open-ended Question Answering with Charts
%A Kantharaj, Shankar
%A Do, Xuan Long
%A Leong, Rixie Tiffany
%A Tan, Jia Qing
%A Hoque, Enamul
%A Joty, Shafiq
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F kantharaj-etal-2022-opencqa
%X 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.
%U https://aclanthology.org/2022.emnlp-main.811
%P 11817-11837
Markdown (Informal)
[OpenCQA: Open-ended Question Answering with Charts](https://aclanthology.org/2022.emnlp-main.811) (Kantharaj et al., EMNLP 2022)
ACL
- Shankar Kantharaj, Xuan Long Do, Rixie Tiffany Leong, Jia Qing Tan, Enamul Hoque, and Shafiq Joty. 2022. OpenCQA: Open-ended Question Answering with Charts. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11817–11837, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.