Code for paper Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach by Zhe Lin, Xiaojun Wan. This paper is accepted by Findings of ACL'21.
License: MIT License
For Quora, there are actually 149,263 samples in total, rather than the data split reported in the paper (129,263\3k\3k). Is there a reason why not to use the full dataset? Thanks.
Hi,
During training, I get the following error:
Traceback (most recent call last):
File "train.py", line 182, in <module>
generation_save_path=args.generation_save_path)
File "/disk/nfs/ostrom/s1717552/btmpg/utils/run.py", line 133, in __call__
self.run()
File "/disk/nfs/ostrom/s1717552/btmpg/utils/run.py", line 100, in run
max_length=self.max_length)
File "/disk/nfs/ostrom/s1717552/btmpg/model/VAE.py", line 206, in round
out_embed = self.embed(self.GS(sentence[:, -1:, :]))
File "/disk/nfs/ostrom/s1717552/btmpg/btmpgenv/lib64/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/disk/nfs/ostrom/s1717552/btmpg/model/gumbleSoftmax.py", line 17, in forward
sigma = min(self.tau_max, (self.tau_max ** (self.n / self.N)))
OverflowError: (34, 'Numerical result out of range')
This happens after a few days of training, around epoch 39 for MSCOCO and epoch 77 for Quora.
The command used was:
python train.py --cuda \
--train_source ./data/qqp_train.src \
--train_target ./data/qqp_train.tgt \
--test_source ./data/qqp_dev.src \
--test_target ./data/qqp_dev.tgt \
--vocab_path ./checkpoints/qqp.vocab \
--batch_size 8 \
--epoch 100 \
--num_rounds 2 \
--max_length 50 \
--clip_length 50 \
--model_save_path ./checkpoints/qqp.model \
--generation_save_path ./outputs/qqp/
When I run the bert-score script, I can get precision, recall and f score, what is the bert score in paper stand for
Could you share the data split of quora and mscoco? I need them for a fair comparison, Thanks.
When I use a checkpoint that I've trained from scratch instead of the checkpoint downloaded from here, performance is ~2 iBLEU lower. The command used to train the model was:
python train.py --cuda \
--train_source ./data/qqp_train.src \
--train_target ./data/qqp_train.tgt \
--test_source ./data/qqp_dev.src \
--test_target ./data/qqp_dev.tgt \
--vocab_path ./checkpoints/qqp.vocab \
--batch_size 8 \
--epoch 100 \
--num_rounds 2 \
--max_length 50 \
--clip_length 50 \
--model_save_path ./checkpoints/qqp.model \
--generation_save_path ./outputs/qqp/
Are there additional hyperparameters that I need to set?
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