Contextualized non-local neural networks for sequence learning
Pengfei Liu, Shuaicheng Chang, Jian Tang, Jackie Chi Kit Cheung
We propose to utilize the concept locality bias to analyze existing neural sequence models.
We analyze two challenges for sequence learning (structure of sentences and contextualized word representations) from the perspective of models’ locality biases, which encourages us to find the complementarity between Transformer and graph neural networks
We draw on the complementary strengths of Transformer and GNNs, proposing a contextualized non-local neural network