“NLP Status Report 2017-1-10”版本间的差异

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| rowspan="6"|2017/1/3
 
| rowspan="6"|2017/1/3
 
|Yang Feng ||
 
|Yang Feng ||
*[[nmt+mn:]] fix the problem of generating unreasonable results of baselines;
+
*[[nmt+mn:]] fixed the problem of generating unreasonable results of baselines;
 
* ran experiments [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/5/50/Nmt_mn_report.pdf report]];
 
* ran experiments [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/5/50/Nmt_mn_report.pdf report]];
* polish and submit the TRP of Moses;
+
* polished and submitted the TRP of Moses;
 
* wrote the code of result analysis (under-going);
 
* wrote the code of result analysis (under-going);
 
||
 
||
 
*[[nmt+mn:]] analyze results;
 
*[[nmt+mn:]] analyze results;
*use the embedding of word2vec;
 
 
*do more experiments
 
*do more experiments
 
|-
 
|-

2017年1月16日 (一) 04:51的最后版本

Date People Last Week This Week
2017/1/3 Yang Feng
  • nmt+mn: fixed the problem of generating unreasonable results of baselines;
  • ran experiments [report];
  • polished and submitted the TRP of Moses;
  • wrote the code of result analysis (under-going);
  • nmt+mn: analyze results;
  • do more experiments
Jiyuan Zhang
  • designed the questionnaire, then discovered some problems and redesigned
  • complete the questionnaire
Andi Zhang
  • helped Jiyuan dealing with poem questionnaires
  • continue this work, may start collecting feedback if all questionnaires are filled out
Shiyue Zhang
  • finished running theano baseline
  • read and understood the beam search in theano baseline
  • started to write the Dynet Chinese Document
  • started to implement beam search to tensorflow baseline
  • finished the beam search implementation
  • go on with Dynet Chinese Document
Guli
  • run nmt with monolingual data
  • bleu computation
  • learn about tensorflow
  • improve my paper
  • analyze experiment results
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