kevinduh / san_mrc

Stochastic Answer Networks (SAN) for Machine Reading Comprehension

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Elmo on Squad2 Not Converging

liuzzi opened this issue · comments

Hi! firstoff, thanks for this awesome repo.

So I've been playing with the new addition of Elmo embeddings with squad 2 and it doesnt seem to ever learn much. accuracy and f1s are all around 50% and loss barely goes down after a certain point. Do you think this is a hyperparam issue? Any ideas?

Thanks

I haven't test on v2 yet, but can you share the log to me? I'll take a look. thanks.

For config I used the same as in the repo, except lowered dropout_p=0.1 and just trained with --elmo_on.

training loss creeps down about .03 per epoch
learning curve looks like this:

[Epoch 0 - dev EM: 49.600 F1: 49.653 (best EM: 49.600 F1: 49.653)]
[Epoch 0 - ACC: 49.9284]
[Epoch 1 - dev EM: 49.701 F1: 49.783 (best EM: 49.701 F1: 49.783)]
[Epoch 1 - ACC: 49.9284]
[Epoch 2 - dev EM: 49.844 F1: 49.856 (best EM: 49.844 F1: 49.856)]
[Epoch 2 - ACC: 49.9284]
[Epoch 3 - dev EM: 50.013 F1: 50.013 (best EM: 50.013 F1: 50.013)]
[Epoch 3 - ACC: 49.9284]
[Epoch 4 - dev EM: 50.021 F1: 50.027 (best EM: 50.021 F1: 50.027)]
[Epoch 4 - ACC: 49.9284]

Thanks for the log.
I worked on Elmo recently applying it with CNTK
Could you share your repo so that I can check where its not working?

All i did was follow the squad2 training directions from the master repo here, and trained with --elmo_on

Hi @liuzzi @namisan,

While training on SQUAD2 (without ELMO) does the following command worked for you without any changes ? I am facing errors regarding the dev_gold labels directory.

python train.py --v2_on --dev_gold data\dev-v2.0.json

Thanks

I don't believe i had problems with the labels directory, although i vaguely remember having to correct some Elmo errors. If i get the chance to test the clean master repo again i'll update you

Sure.
I cloned the repo a couple of days back and was able to reproduce the results on v1.1, both with and without Elmo but got stuck on v2.0.

@liuzzi

For me the model is converging with Elmo on Squad2, though EM and F1 scores are better without Elmo embeddings on same set of hyperparameters.

I didn't see the converge issues if you use an appropriate learning rate. Thus, I'll close this one.