“NLP Status Report 2017-4-5”版本间的差异

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!Date !! People !! Last Week !! This Week
 
!Date !! People !! Last Week !! This Week
 
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| rowspan="6"|2017/3/27
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| rowspan="6"|2017/4/5
 
|Yang Feng ||
 
|Yang Feng ||
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*tested for the baseline but cannot get the reasonable result.
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* Got the sampled 100w good data and ran Moses (BLEU: 30.6)
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*debug the baseline to try to reproduce the good result but failed.
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* Reimplemented the idea of ACL (added some optimization to the previous code) and check the performance in the following gradual steps: 1. use s_i-1 as memory query; 2. use s_i-1+c_i as memory query; 3. use y as the memory states for attention; 4. use y + smt_attentions * h as memory states for attention.
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*fixed the problem of nan in alpha-gamma method but the result is not good.
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* ran experiments for the above steps but the loss was inf. I am looking for reasons.
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*changed the calculation of probability for alpha-gamma method but the result is neither good.
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*ran Moses for cwmt zh-en translation, but the training data is case-sensitive, so need to rerun.
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*rerun Moses for cwmt zh-en and cs-en
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*do experiments and write the paper
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*decide to use tensorflow or theano
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*run experiments based on the chosen platform
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|Jiyuan Zhang ||
 
|Jiyuan Zhang ||
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* I did nothing (I found my ACL mark was at borderline, so I didn't have a mind to work)
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* I did keyword expansion on the qx's model
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*fixed some bugs
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*read two papers
 
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*improve the effect of the qx's model
 
*improve the effect of the qx's model
 
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|Andi Zhang ||
 
|Andi Zhang ||
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*fixed the bug, turns out it rises from the unfamiliarity with numpy.resize() function
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*revise the original oov model so that it can automatically detect oov words and translate them
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*the demo model can deal with oov problem(both source word and target word are oov)
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*deal with the situation that source word is oov but target word is not oov first
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*it didn't predict right
 
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*some paper work about graduation design
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*make the model work as what we wanted
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*run some experiments using theano on old data set and new zh2en from lihang
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*deal with the situation that source word is oov and target word is also oov, then other situations
 
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|Shiyue Zhang ||  
 
|Shiyue Zhang ||  
 
* got a reasonable baseline on big zhen data
 
* got a reasonable baseline on big zhen data
 
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* implement mem model on this baseline, test on big data
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* implement mem model on this baseline, and test on big data
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* implement
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|Peilun Xiao ||
 
|Peilun Xiao ||

2017年4月5日 (三) 02:16的最后版本

Date People Last Week This Week
2017/4/5 Yang Feng
  • Got the sampled 100w good data and ran Moses (BLEU: 30.6)
  • Reimplemented the idea of ACL (added some optimization to the previous code) and check the performance in the following gradual steps: 1. use s_i-1 as memory query; 2. use s_i-1+c_i as memory query; 3. use y as the memory states for attention; 4. use y + smt_attentions * h as memory states for attention.
  • ran experiments for the above steps but the loss was inf. I am looking for reasons.
  • do experiments and write the paper
Jiyuan Zhang
  • I did keyword expansion on the qx's model
  • fixed some bugs
  • read two papers
  • improve the effect of the qx's model
Andi Zhang
  • revise the original oov model so that it can automatically detect oov words and translate them
  • deal with the situation that source word is oov but target word is not oov first
  • it didn't predict right
  • make the model work as what we wanted
  • deal with the situation that source word is oov and target word is also oov, then other situations
Shiyue Zhang
  • got a reasonable baseline on big zhen data
  • implement mem model on this baseline, and test on big data
Peilun Xiao
取自“http://index.cslt.org/mediawiki/index.php?title=NLP_Status_Report_2017-4-5&oldid=26612”