Segmentation fault when running the constituency tree-lstm

[yukiar]

Similar to the Issue #8 , "Segmentation fault (core dumped)" encountered at a random epoch when using the constituency tree-lstm.
I'm running several trials with the SICK dataset as the command provided without any change in the codes:
th relatedness/main.lua --model constituency
How can I solve this issue?

Here is a sample of the console output:

--------------------------------------------------------------------------------	
Constituency Tree LSTM for Semantic Relatedness	
--------------------------------------------------------------------------------	
loading word embeddings	
unk count = 5	
loading datasets	
num train = 4500
num dev   = 500
num test  = 4927
--------------------------------------------------------------------------------	
model configuration	
--------------------------------------------------------------------------------	
max epochs = 10
num params                = 241655
num compositional params  = 226350
word vector dim           = 300
Tree-LSTM memory dim      = 150
regularization strength   = 1.00e-04
minibatch size            = 25
learning rate             = 5.00e-02
word vector learning rate = 0.00e+00
parse tree type           = constituency
sim module hidden dim     = 50
--------------------------------------------------------------------------------	
Training model	
--------------------------------------------------------------------------------	
-- epoch 1
 [======================================== 4500/4500 ==================================>]  Tot: 9m11s | Step: 122ms     
-- finished epoch in 551.54s
 [======================================== 500/500 ====================================>]  Tot: 20s578ms | Step: 45ms   
-- dev score: 0.6270
-- epoch 2
Segmentation fault (core dumped)========== 4151/4500 ===========================>.......]  ETA: 41s839ms | Step: 119ms 

[smilenrhyme]

@yukiar
+1 facing the same issue. Did you find some solution for this ?

@kaishengtai Could you please provide some pointers here.

Thanks a lot !!