Co-regularized alignment for unsupervised domain adaptation
Abhishek Kumar, Kahini Wadhawan, et al.
NeurIPS 2018
Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information for rich inference. We introduce a new method for better connecting global evidence, which forms more complex graphs compared to DAGs. To perform evidence integration on our graphs, we investigate two recent graph neural networks, namely graph convolutional network (GCN) and graph recurrent network (GRN). Experiments on two standard datasets show that richer global information leads to better answers. Our approach shows highly competitive performances on these datasets without deep language models (such as ELMo).
Abhishek Kumar, Kahini Wadhawan, et al.
NeurIPS 2018
Ademir Ferreira Da Silva, Maciel Zortea, et al.
IGARSS 2024
Maddalena Torricelli, Mauro Martino, et al.
WebSci 2024
Shashank Srikant, Sijia Liu, et al.
ICLR 2021