Yingting Li
ABSTRACT
The rise of the counterfactual concept promoted the study of reasoning, and we applied it to Knowledge Base Question Answering (KBQA) multi-hop reasoning as a way of data augmentation for the first time. Intuitively, we propose a model-agnostic Counterfactual Samples Synthesizing(CSS) training scheme. The CSS uses two augmentation methods Q-CSS and T-CSS to augment the training set. That is, for each training instance, we create two augmented instances, one per augmentation method. Furthermore, perform the Dynamic Answer Equipment(DAE) algorithm to dynamically assign ground-truth answers for the expanded question, constructing counterfactual examples. After training with the supplemented examples, the KBQA model can focus on all key entities and words, which significantly improved model’s sensitivity. Experimental verified the effectiveness of CSS and achieved consistent improvements across settings with different extents of KB incompleteness.
- Long Chen, Xin Yan, Jun Xiao, Hanwang Zhang, Shiliang Pu, and Yueting Zhuang. 2020. Counterfactual Samples Synthesizing for Robust Visual Question Answering. In CVPR.Google Scholar
- R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In 2017 IEEE International Conference on Computer Vision (ICCV). 618–626. https://doi.org/10.1109/ICCV.2017.74Google ScholarCross Ref
- Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, and William W Cohen. 2018. Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text. EMNLP (2018).Google Scholar
- Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, and William Yang Wang. 2019. Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 4258–4264. https://doi.org/10.18653/v1/P19-1417Google ScholarCross Ref
- Counterfactual-Augmented Data for Multi-Hop Knowledge Base Question Answering
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