Difference between revisions of "NLP Status Report 2016-12-19"

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(以“{| class="wikitable" !Date !! People !! Last Week !! This Week |- | rowspan="6"|2016/12/12 |Yang Feng || *s2smn: installed tensorflow and ran a toy example (solv...”为内容创建页面)
 
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| rowspan="6"|2016/12/12
 
| rowspan="6"|2016/12/12
 
|Yang Feng ||
 
|Yang Feng ||
*[[s2smn:]] installed tensorflow and ran a toy example (solved problems: version conflict and memory exhausted)
+
*[[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]]
*wrote the code of the memory  network part
+
*wrote part of the code of mn.
*[[Huilan:]] prepared for periodical report and system submission.
+
*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]]
 +
*[[Huilan:]] fixed the problem of syntax-based translation.
 +
*sort out the system and corresponding documents.
 
||
 
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*[[s2smn:]] finish the manual of nmt tensorflow
 
*[[s2smn:]] finish the manual of nmt tensorflow

Revision as of 05:10, 19 December 2016

Date People Last Week This Week
2016/12/12 Yang Feng
  • s2smn: wrote the manual of s2s with tensorflow [nmt-manual]
  • wrote part of the code of mn.
  • wrote the manual of Moses [moses-manual]
  • Huilan: fixed the problem of syntax-based translation.
  • sort out the system and corresponding documents.
  • s2smn: finish the manual of nmt tensorflow
  • Huilan: system submission
Jiyuan Zhang
  • attempted to use memory model to improve the atten model of bad effect
  • With the vernacular as the input,generated poem by local atten model[1]
  • Modified working mechanism of memory model(top1 to average)
  • help andi
  • improve poem model
Andi Zhang
  • prepared a paraphrase data set that is enumerated from a previous one (ignoring words like "啊呀哈")
  • worked on coding bidirectional model under tensorflow, met with NAN problem
  • ignore NAN problem for now, run it on the same data set used in Theano
Shiyue Zhang
  • finished tsne pictures, and discussed with teachers
  • tried experiments with 28-dim mem, but found almost all of them converged to baseline
  • returned to 384-dim mem, which is still slightly better than basline.
  • found the problem of action mem, one-hot vector is not proper.
  • [report]
  • change one-hot vector to (0, -10000.0, -10000.0...)
  • try 1-dim gate
  • try max cos
Guli
  • install and run moses
  • prepare thesis report
  • read papers about Transfer learning and solving OOV
Peilun Xiao
  • Read a paper about document classification wiht GMM distributions of word vecotrs and try to code it in python
  • Use LDA to reduce the dimension of the text in r52、r8 and contrast the performance of classification
  • Use LDA to reduce the dimension of the text in 20news and webkb