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Graph Convolutional Networks (GCN) with BERT for Coreference Resolution Task [Pytorch][DGL]

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bert bert-model coreference-resolution gcn gnn graph-convolutional-networks graph-neural-networks nlp pytorch

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gcn-with-bert's Issues

Custom training data for RGCN-with-BERT

Hi all,
I was wondering if it is possible to use custom data that one can prepare themselves for training this model. If so, how does one do this with RGCN-with-BERT? Will it convert a txt file to the right format or does one have to convert it to a ConLL file first? Can it be ConLL-U? Thank you very much.

work on entity alignment

does r-gcn works on entity alignment (existing R-GCN seems only focus on classification and link prediction)?
If yes, May I know do you have any working code on this?

the dataset using in Main Model.ipynb

token_lst = pickle.load(open('token_lst_wto_padding.pkl', "rb"))
offsets_lst = pickle.load(open('offsets_lst.pkl', "rb"))
This two statements call for two pkl files which not offer in this repo. Can you give a sample case of these two pkl files? It would be better if you can upload :)

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