Question about the adjacent matrix

Hi~

In your abstract, you said “In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs”, but I don’s see how do you make use of the dependency tree.

According to Equation (2), seems you just construct the $\tilde{A}$ by using the input embeddings, so where do you make use of the original adjacency matrix $A$?

In your code, I still cannot confirm how do you make use of $A$, which is adj in your code, right? https://github.com/Cartus/AGGCN_TACRED/blob/master/model/aggcn.py#L175

Could you please show me the details? And by the way, that is the matrix $V$ in your Equation (2)? I can not find the definition of $V$ neither.

[Cartus]

Hi @liuyijiang1994 ,

Thanks for the question. Actually they are basically the same. I read a paper few weeks ago. The paper discusses about the GNNs and Transformer model, which might be useful. They also have a nice summary of these works including different GNNs and different self-attention methods.

Contextualized Non-local Neural Networks for Sequence Learning https://arxiv.org/abs/1811.08600#

[speedcell4]

@TranVanHien Sorry, I can not, I realized I can never figure out these complicated experiment settings and then I decided to give up doing relation extraction task two months ago