Paper accepted by AAAI-2020
This is a followup paper of "Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism" ACL2018 CopyRE
This repo only contains CopyRE' part. MTL part is very old and messy, we are not going to release it. In other words, this repo only uses the last token of the entity for training and evaluation. If you want CopyMTL to manipulate complete entities, we suggest using pytorch-crf to implement the sequence labeling module for encoder. The dataset from CopyRE does not support MTL as well, because it lose the NER annotation. You'll have to re-preprocessing the data from scratch to gain full entity, rather than the links below.
python3
pytorch 0.4.0 -- 1.3.1
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
python main.py --gpu 0 --mode train --cell lstm --decoder_type one
python main.py --gpu 0 --mode test --cell lstm --decoder_type one