Difference between revisions of "NLP Status Report 2016-12-26"

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| rowspan="6"|2016/12/26
 
| rowspan="6"|2016/12/26
 
|Yang Feng ||
 
|Yang Feng ||
*[[s2smn:]] wrote the manual of s2s with tensorflow [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/5/51/Nmt-tensorflow-mannua-yfeng.pdf nmt-manual]]
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*[[s2smn:]] read six papers to fix the details of our model;
*wrote part of the code of mn.
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*wrote the proposal of lexical memory and discussed the details with Teach Wang;
*wrote the manual of Moses [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/92/Moses%E6%93%8D%E4%BD%9C%E6%89%8B%E5%86%8C--%E5%86%AF%E6%B4%8B.pdf moses-manual]]
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*finished coding of only adding attention to the decoder and under debugging;
*[[Huilan:]] fixed the problem of syntax-based translation.
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*refine Moses manual [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/92/Moses%E6%93%8D%E4%BD%9C%E6%89%8B%E5%86%8C--%E5%86%AF%E6%B4%8B.pdf manual]] ;
*sort out the system and corresponding documents.
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*prepare the dictionary for the memory loading;
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*[[Huilan:]] documentation
 
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*[[s2smn:]] finish the code of adding mn.
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*[[s2smn:]] run the ecperiments.
*[[Huilan:]] handover.
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*[[rnng+mn:]] try to find the problem.
 
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|Jiyuan Zhang ||
 
|Jiyuan Zhang ||
*coded tone_model,but had some trouble
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*integrated tone_model to attention_model for insteading manul rule,but the effect wasn't good
*run global_attention_model that decodes four sentences, [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/d/d5/Four_local_atten.pdf four][http://cslt.riit.tsinghua.edu.cn/mediawiki/images/0/05/Five_local_attention.pdf five]generated by local_attention model
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*replacing all_pz rule with half_pz
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*token a classical Chinese as input,generated poem [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/3/33/Story_input.pdf]
 
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*improve poem model   
 
*improve poem model   
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*coded a script for bleu scoring, which tests the five checkpoints auto created by training process and save the one with best performance
 
*coded a script for bleu scoring, which tests the five checkpoints auto created by training process and save the one with best performance
 
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*extract encoder outputs
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*
 
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|Shiyue Zhang ||  
 
|Shiyue Zhang ||  
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|Guli ||
 
|Guli ||
*read papers about Transfer learning and solving OOV
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* finished the first draft of the survey
*conducted comparative test
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* voice tagging 
*writing survey
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* complete the first draft of the survey 
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* morpheme-based nmt
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* improve nmt with monolingual data
 
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|Peilun Xiao ||
 
|Peilun Xiao ||

Latest revision as of 05:16, 26 December 2016

Date People Last Week This Week
2016/12/26 Yang Feng
  • s2smn: read six papers to fix the details of our model;
  • wrote the proposal of lexical memory and discussed the details with Teach Wang;
  • finished coding of only adding attention to the decoder and under debugging;
  • refine Moses manual [manual] ;
  • prepare the dictionary for the memory loading;
  • Huilan: documentation
Jiyuan Zhang
  • integrated tone_model to attention_model for insteading manul rule,but the effect wasn't good
  • replacing all_pz rule with half_pz
  • token a classical Chinese as input,generated poem [1]
  • improve poem model
Andi Zhang
  • coded to output encoder outputs and correspoding source & target sentences(ids in dictionaries)
  • coded a script for bleu scoring, which tests the five checkpoints auto created by training process and save the one with best performance
Shiyue Zhang
  • tried to add true action info when training gate, which got better results than no true actions, but still not very good.
  • tried different scale vectors, and found setting >=-5000 is good
  • tried to change cos to only inner product, and inner product is better than cos.
  • [report]
  • read 3 papers [[2]] [[3]] [[4]]
  • trying the joint training, which got a problem of optimization.
  • try the joint training
  • read more papers and write a summary
Guli
  • finished the first draft of the survey
  • voice tagging
  • morpheme-based nmt
  • improve nmt with monolingual data
Peilun Xiao
  • learned tf-idf algorithm
  • coded tf-idf alogrithm in python,but found it not worked well
  • tried to use small dataset to test the program
  • use sklearn tfidf to test the dataset