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Found 3 papers, 1 papers with code
A Bi-consolidating Model for Joint Relational Triple Extraction
In this paper, based on a two-dimensional sentence representation, a bi-consolidating model is proposed to address this problem by simultaneously reinforcing the local and global semantic features relevant to a relation triple.
Interpretable Neural Computation for Real-World Compositional Visual Question Answering
There are two main lines of research on visual question answering (VQA): compositional model with explicit multi-hop reasoning, and monolithic network with implicit reasoning in the latent feature space.
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering
However, there are few works studying the data augmentation problem for VQA and none of the existing image based augmentation schemes (such as rotation and flipping) can be directly applied to VQA due to its semantic structure -- an $\langle image, question, answer\rangle$ triplet needs to be maintained correctly.
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