Difference between revisions of "NLP Status Report 2016-09-26"

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*prepared the phrasal check of Huilan
 
*prepared the phrasal check of Huilan
 
*prepared and discussed the main idea of next work
 
*prepared and discussed the main idea of next work
*learned Lua and Cuda and read the code of MemNN
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*learned Lua and Torch and read the code of MemNN
 
*read the code of rnng
 
*read the code of rnng
 
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*run the experiments of MN grammar
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*modify the code to MN grammar and run the experiments
 
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|Jiyuan Zhang ||
 
|Jiyuan Zhang ||
*ran two versions of the code that qixin gave me,the last version of the code of the result   is good
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*ran two versions of the code that qixin gave me,the last version of the result is good
 
*perfected my poem's code
 
*perfected my poem's code
*Results for all versions of the code[[]]
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*Results for all versions of the code[[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/7/72/Theme.pdf here]]
 
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*generate the same level poem compared to qx’s
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*perfect my code  according to up-to-date version of qixin's code
 
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|Aodong Li ||  
 
|Aodong Li ||  
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|Shiyue Zhang ||  
 
|Shiyue Zhang ||  
*ran rnng code successfully
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*ran rnng code successfully: the result of discriminative model is a bit better than original paper; the generative model has not fully trained, so the performance is worse.
 
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*get generative model f1 score
 
*get generative model f1 score

Latest revision as of 06:18, 26 September 2016

Date People Last Week This Week
2016/09/26 Yang Feng
  • prepared the phrasal check of Huilan
  • prepared and discussed the main idea of next work
  • learned Lua and Torch and read the code of MemNN
  • read the code of rnng
  • modify the code to MN grammar and run the experiments
Jiyuan Zhang
  • ran two versions of the code that qixin gave me,the last version of the result is good
  • perfected my poem's code
  • Results for all versions of the code[here]
  • perfect my code according to up-to-date version of qixin's code
Aodong Li
Andi Zhang
  • read the sorce code of MemN2N
  • installed torch
  • try to fit the params of rnng into memn2n
Shiyao Li
Shiyue Zhang
  • ran rnng code successfully: the result of discriminative model is a bit better than original paper; the generative model has not fully trained, so the performance is worse.
  • get generative model f1 score
  • get the states of each timestep