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        <title>STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering</title>
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    <abstract>Chart Question Answering (CQA) is the task of answering natural language questions about visualisations in the chart image. Recent solutions, inspired by VQA approaches, rely on image-based attention for question/answering while ignoring the inherent chart structure. We propose STL-CQA which improves the question/answering through sequential elements localization, question encoding and then, a structural transformer-based learning approach. We conduct extensive experiments while proposing pre-training tasks, methodology and also an improved dataset with more complex and balanced questions of different types. The proposed methodology shows a significant accuracy improvement compared to the state-of-the-art approaches on various chart Q/A datasets, while outperforming even human baseline on the DVQA Dataset. We also demonstrate interpretability while examining different components in the inference pipeline.</abstract>
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%0 Conference Proceedings
%T STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering
%A Singh, Hrituraj
%A Shekhar, Sumit
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F singh-shekhar-2020-stl
%X Chart Question Answering (CQA) is the task of answering natural language questions about visualisations in the chart image. Recent solutions, inspired by VQA approaches, rely on image-based attention for question/answering while ignoring the inherent chart structure. We propose STL-CQA which improves the question/answering through sequential elements localization, question encoding and then, a structural transformer-based learning approach. We conduct extensive experiments while proposing pre-training tasks, methodology and also an improved dataset with more complex and balanced questions of different types. The proposed methodology shows a significant accuracy improvement compared to the state-of-the-art approaches on various chart Q/A datasets, while outperforming even human baseline on the DVQA Dataset. We also demonstrate interpretability while examining different components in the inference pipeline.
%R 10.18653/v1/2020.emnlp-main.264
%U https://aclanthology.org/2020.emnlp-main.264/
%U https://doi.org/10.18653/v1/2020.emnlp-main.264
%P 3275-3284

Markdown (Informal)

[STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering](https://aclanthology.org/2020.emnlp-main.264/) (Singh & Shekhar, EMNLP 2020)

• STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering (Singh & Shekhar, EMNLP 2020)

ACL

• Hrituraj Singh and Sumit Shekhar. 2020. STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3275–3284, Online. Association for Computational Linguistics.