RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic

Author:

Ahmed SaleemORCID,Jawade Bhavin,Pandey Shubham,Setlur SrirangarajORCID,Govindaraju VenuORCID

Publisher

Springer Nature Switzerland

Reference36 articles.

1. Ahmed, S., Davila, K., Setlur, S., Govindaraju, V.: Equation attention relationship network (earn) : a geometric deep metric framework for learning similar math expression embedding. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 6282–6289 (2021). https://doi.org/10.1109/ICPR48806.2021.9412619

2. Appalaraju, S., Jasani, B., Kota, B.U., Xie, Y., Manmatha, R.: DocFormer: end-to-end transformer for document understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 993–1003 (2021)

3. Barceló, P., Kostylev, E.V., Monet, M., Pérez, J., Reutter, J., Silva, J.P.: The logical expressiveness of graph neural networks. In: 8th International Conference on Learning Representations (ICLR 2020) (2020)

4. Battaglia, P.W., et al.: Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)

5. Besold, T.R., et al.: Neural-symbolic learning and reasoning: a survey and interpretation. CoRR abs/1711.03902 (2017)

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1. DocTabQA: Answering Questions from Long Documents Using Tables;Lecture Notes in Computer Science;2024

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