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Optimized decoder of Seq2seq for Joint Entity and Relation Extraction

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jbjeong91/OptimSeq2seq

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Note

The codes is for my paper work, and it is modified based on the CopyMTL repo. Some parts from original repo have been deleted and modified.

Environment

python3

pytorch 0.4.0 -- 1.3.1

Modify the Data path

This repo initially contain webnlg, you can run the code directly. NYT dataset need to be downloaded and to be placed in proper path. see const.py.

The pre-processed data is avaliable in:

WebNLG dataset: https://drive.google.com/open?id=1zISxYa-8ROe2Zv8iRc82jY9QsQrfY1Vj

NYT dataset: https://drive.google.com/open?id=10f24s9gM7NdyO3z5OqQxJgYud4NnCJg3

Run

  • Train on GPU or CPU

python main.py --gpu_use True --mode train --cell lstm

python main.py --gpu_use False --mode train --cell lstm

  • Test on GPU or CPU

python main.py --gpu_use True --mode test --cell lstm

python main.py --gpu_use False --mode test --cell lstm

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Optimized decoder of Seq2seq for Joint Entity and Relation Extraction

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