<?xml version="1.0"?>
<?xml-stylesheet type="text/css" href="http://index.cslt.org/mediawiki/skins/common/feed.css?303"?>
<feed xmlns="http://www.w3.org/2005/Atom" xml:lang="zh-cn">
		<id>http://index.cslt.org/mediawiki/index.php?action=history&amp;feed=atom&amp;title=Nlp-progress-2016-04</id>
		<title>Nlp-progress-2016-04 - 版本历史</title>
		<link rel="self" type="application/atom+xml" href="http://index.cslt.org/mediawiki/index.php?action=history&amp;feed=atom&amp;title=Nlp-progress-2016-04"/>
		<link rel="alternate" type="text/html" href="http://index.cslt.org/mediawiki/index.php?title=Nlp-progress-2016-04&amp;action=history"/>
		<updated>2026-04-14T15:05:07Z</updated>
		<subtitle>本wiki的该页面的版本历史</subtitle>
		<generator>MediaWiki 1.23.3</generator>

	<entry>
		<id>http://index.cslt.org/mediawiki/index.php?title=Nlp-progress-2016-04&amp;diff=20200&amp;oldid=prev</id>
		<title>Cslt：以“===Similar questions senetence vector model training with RNN/LSTM and the attention RNN/LSTM chatting model training (Tianyi Luo)=== --------------------2016-04-22...”为内容创建页面</title>
		<link rel="alternate" type="text/html" href="http://index.cslt.org/mediawiki/index.php?title=Nlp-progress-2016-04&amp;diff=20200&amp;oldid=prev"/>
				<updated>2016-05-11T12:44:51Z</updated>
		
		<summary type="html">&lt;p&gt;以“===Similar questions senetence vector model training with RNN/LSTM and the attention RNN/LSTM chatting model training (Tianyi Luo)=== --------------------2016-04-22...”为内容创建页面&lt;/p&gt;
&lt;p&gt;&lt;b&gt;新页面&lt;/b&gt;&lt;/p&gt;&lt;div&gt;===Similar questions senetence vector model training with RNN/LSTM and the attention RNN/LSTM chatting model training (Tianyi Luo)===&lt;br /&gt;
--------------------2016-04-22&lt;br /&gt;
* Speed up process of the test performance about theano version of Generationg the similar questions' vectors based on RNN.&lt;br /&gt;
--------------------2016-04-21&lt;br /&gt;
* Finish helping Teacher Wang to prepare for text group's presentation(Tang poetry and Songci generation and Intelligent QA system) for Tsinghua University's 105 anniversary.&lt;br /&gt;
* Submit our IJCAI paper to arxiv. (Solve a big problem about submitting the paper including Chinese chacracters. [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/How_to_submit_the_latex_files_including_Chinese_characters_to_arxiv Solution])&lt;br /&gt;
* Optimize theano version of Generationg the similar questions' vectors based on RNN.&lt;br /&gt;
--------------------2016-04-20&lt;br /&gt;
* Finish submiting the camera version paper of IJCAI 2016.&lt;br /&gt;
* Update the version of Technical Report about Chinese Song Iambics generation.&lt;br /&gt;
--------------------2016-04-19&lt;br /&gt;
* Optimize theano version of Generationg the similar questions' vectors based on RNN.&lt;br /&gt;
--------------------2016-04-18&lt;br /&gt;
* Optimize theano version of Generationg the similar questions' vectors based on RNN.&lt;br /&gt;
* Finish implementing theano version of LSTM Max margin vector training.&lt;br /&gt;
&lt;br /&gt;
===Reproduce DSSM Baseline (Chao Xing)===&lt;br /&gt;
: 2016-04-28 : Given a talk to text team for some recently paper.&lt;br /&gt;
               Knowledge Base Completion via Search-Based Question Answering : [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b1/Knowledge_Base_Completion_via_Search-Based_Question_Answering_-_Report.pdf pdf]&lt;br /&gt;
               Open Domain Question Answering via Semantic Enrichment  : [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/1/15/Open_Domain_Question_Answering_via_Semantic_Enrichment_-_Report.pdf pdf]&lt;br /&gt;
               A Neural Conversational Model : [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/1/15/A_Neural_Conversational_Model_-_Report.pdf pdf]&lt;br /&gt;
               And given a tiny results for CNN-DSSM in huilan's weekly report.&lt;br /&gt;
: 2016-04-27 : Code Multi-layer CNN, suffered from memory error in GPU in tensorflow.&lt;br /&gt;
               So I run such test on CPU, should slow.&lt;br /&gt;
: 2016-04-26 : Code done tricky &amp;amp; analysis such tricky.&lt;br /&gt;
: 2016-04-25 : Find a tricky to improve accuracy given by Tianyi.&lt;br /&gt;
             : Code for this tricky.&lt;br /&gt;
: 2016-04-23 : Set a series of experiment set.&lt;br /&gt;
               1. Try deep CNN-DSSM, current model just follow proposed model contain one convolution layer, need to be a tuneable parameter.&lt;br /&gt;
               2. Test whether mixture data effective to current model and deep CDSSM.&lt;br /&gt;
               3. Code Recurrent CNN-DSSM (new approach.)&lt;br /&gt;
: 2016-04-22 : Find a problem : Use labs' gpu machine 970 iteration per time is 1537 second but huilan's server is just 7 second.&lt;br /&gt;
               Achieve reasonable results when apply max-margin method to CNN-DSSM model.&lt;br /&gt;
: 2016-04-21 : True DSSM model doesn't work well, analysis as below:&lt;br /&gt;
                1. Not exactly reproduce DSSM model, because the original one is English version, I just adapt it to Chinese but after word segmentation. &lt;br /&gt;
                   So the input is tri-gram words not tri-gram letter.&lt;br /&gt;
                2. Our dataset far from rich, because of we do not use pre-trained word vectors as initial vectors, we can hardly achieve good performance.&lt;br /&gt;
             : Request&lt;br /&gt;
                1. As we have rich pre-trained word vectors, maybe CDSSM or RDSSM corrected to our task.&lt;br /&gt;
                2. Different length of sequences seek to be fixed dimension vectors, just CNN and RNN can do such things, DNN can not do it by using &lt;br /&gt;
                  fix length of word vectors&lt;br /&gt;
             : Coding done CDSSM. Test for it's performance.&lt;br /&gt;
                One problem : When you install tensorflow by pip 0.8.0 and you want to use conv2d function by gpu, you need make sure you had already &lt;br /&gt;
                             install your cudnn's version as 4.0 not lastest 5.0.&lt;br /&gt;
: 2016-04-20 : Find reproduced DSSM model's bug, fix it.&lt;br /&gt;
: 2016-04-19 : Code mixture data model by less memory dependency done. Test it's performance.&lt;br /&gt;
: 2016-04-18 : Code mixture data model.&lt;br /&gt;
: 2016-04-16 : Code mixture data model, but face to memory error. Dr. Wang help me fix it.&lt;br /&gt;
: 2016-04-15 : Share Papers. Investigation a series of DSSM papers for future work. And show our intern students how to do research.&lt;br /&gt;
             : Original DSSM model : Learning Deep Structured Semantic Models for Web Search using Clickthrough Data [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/4/45/2013_-_Learning_Deep_Structured_Semantic_Models_for_Web_Search_using_Clickthrough_Data_-_Report.pdf pdf]&lt;br /&gt;
             : CNN based DSSM model : A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/b7/2014_-_A_Latent_Semantic_Model_with_Convolutional-Pooling_Structure_for_Information_Retrieval_-_Report.pdf pdf]&lt;br /&gt;
             : Use DSSM model for a new area : Modeling Interestingness with Deep Neural Networks [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/1/1f/2014_-_Modeling_Interestingness_with_Deep_Neural_Networks_-_Report.pdf pdf]&lt;br /&gt;
             : Latest approach for LSTM + RNN DSSM model : SEMANTIC MODELLING WITH LONG-SHORT-TERM MEMORY FOR INFORMATION RETRIEVAL [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/2/24/2015_-_SEMANTIC_MODELLING_WITH_LONG-SHORT-TERM_MEMORY_FOR_INFORMATION_RETRIEVAL_-_Report.pdf pdf]&lt;br /&gt;
&lt;br /&gt;
: 2016-04-14 : Test dssm-dnn model, code dssm-cnn model.&lt;br /&gt;
               Continue investigate deep neural question answering system.&lt;br /&gt;
: 2016-04-13 : test dssm model, investigate deep neural question answering system.&lt;br /&gt;
             : Share theano ppt [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Theano-RBM.pptx theano]&lt;br /&gt;
             : Share tensorflow ppt [http://cslt.riit.tsinghua.edu.cn/mediawiki/index.php/%E6%96%87%E4%BB%B6:Tensorflow.pptx tensorflow]&lt;br /&gt;
: 2016-04-12 : Write done dssm tensor flow version.&lt;br /&gt;
: 2016-04-11 : Write tensorflow toolkit ppt for intern student.&lt;br /&gt;
: 2016-04-10 : Learn tensorflow toolkit.&lt;br /&gt;
: 2016-04-09 : Learn tensorflow toolkit.&lt;br /&gt;
: 2016-04-08 : Finish theano version.&lt;br /&gt;
&lt;br /&gt;
===Deep Poem Processing With Image (Ziwei Bai)===&lt;br /&gt;
: 2016-04-20 :combine my program with Qixin Wang's&lt;br /&gt;
: 2016-04-10 : web spider to catch a thousand pices of images.&lt;br /&gt;
: 2016-04-13 :1、download theano for python2.7。  2.debug cnn.py&lt;br /&gt;
: 2016-04-15 :web spider to catch 30 thousands pices of images and store them into a matrix&lt;br /&gt;
: 2016-04-16 :modify the code of CNN and spider&lt;br /&gt;
: 2016-04-17 :train convouloutional neural network&lt;br /&gt;
&lt;br /&gt;
===RNN Piano Processing (Jiyuan Zhang)===&lt;br /&gt;
:2016-4-12：select appropriate  midis and run rnnrbm model&lt;br /&gt;
:2016-4-13：view  rnnrbm model‘s  code&lt;br /&gt;
:2016-4-14~15:coding to select 4/4 beat of midis&lt;br /&gt;
:2016-4-17~22:run data, failed several times ，then modify code  and  view rnnrbm model's code&lt;br /&gt;
:2016-4-25~29:replace rnnrbm  with lstmrbm, then run lstmrbm's model&lt;br /&gt;
&lt;br /&gt;
===Question &amp;amp; Answering (Aiting Liu)===&lt;br /&gt;
: 2016-04-24 : make my biweekly report&lt;br /&gt;
: 2016-04-23 : read Fader's paper (2011)&lt;br /&gt;
: 2016-04-20 : read Fader's paper (2013) &lt;br /&gt;
: 2016-04-15 : learn dssm and sent2vec&lt;br /&gt;
: 2016-04-16 : try to figure out how the PARALAX dataset is constructed&lt;br /&gt;
: 2016-04-17 : download the PARALAX dataset and try to turn it into what we want it to be&lt;/div&gt;</summary>
		<author><name>Cslt</name></author>	</entry>

	</feed>