Discovering Multimodal Hierarchical Structures with Graph Neural Networks for Multi-modal and Multi-hop Question Answering

Author:

Zhang Qing,Lv Haocheng,Liu Jie,Chen Zhiyun,Duan Jianyong,Xv Mingying,Wang Hao

Publisher

Springer Nature Singapore

Reference19 articles.

1. Chang, Y., Narang, M., Suzuki, H., Cao, G., Gao, J., Bisk, Y.: WebQA: multihop and multimodal QA. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16495–16504 (2022)

2. Oguz, B., et al.: UniK-QA: unified representations of structured and unstructured knowledge for open-domain question answering. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 1535–1546, July 2022

3. Qi, P., Lee, H., Sido, T., Manning, C.D.: Answering open-domain questions of varying reasoning steps from text. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3599–3614, November 2021

4. Smith, D.A., Smith, N.A.: Probabilistic models of nonprojective dependency trees. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 132–140, June 2007

5. Koo, T., Globerson, A., Carreras Pérez, X., Collins, M.: Structured prediction models via the matrix-tree theorem. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 141–150 (2007)

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