“NLP Status Report 2017-6-5”版本间的差异

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   Shrink output vocab from 30000 to 20000 and best result is 31.53
 
   Shrink output vocab from 30000 to 20000 and best result is 31.53
 
   Train the model with 40 batch size and best result until now is 30.63
 
   Train the model with 40 batch size and best result until now is 30.63
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* test more checkpoints on model trained with batch = 40
 
* test more checkpoints on model trained with batch = 40
 
* train model with reverse output
 
* train model with reverse output
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|Shiyue Zhang ||  
 
|Shiyue Zhang ||  

2017年6月5日 (一) 05:45的版本

Date People Last Week This Week
2017/6/5 Jiyuan Zhang
Aodong LI
  • Small data:
 Only make the English encoder's embedding constant -- 45.98
 Only initialize the English encoder's embedding and then finetune it -- 46.06
 Share the attention mechanism and then directly add them -- 46.20
  • big data baseline bleu = 30.83
  • Fixed three embeddings
 Shrink output vocab from 30000 to 20000 and best result is 31.53
 Train the model with 40 batch size and best result until now is 30.63
  • test more checkpoints on model trained with batch = 40
  • train model with reverse output
Shiyue Zhang
Shipan Ren