NLP Status Report 2016-12-26 - 版本历史

2016年12月26日 (一) 05:16 Zhangjy

2016-12-26T05:16:37Z

2016-12-26T05:15:56Z

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2016-12-26T01:23:17Z

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 *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
+*token a classical Chinese as input,generated poem [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/3/33/Story_input.pdf]
 ||
 *improve poem model

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 |-
 |Jiyuan Zhang ||
-*coded tone_model,but had some trouble
+*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
+*replacing　all_pz rule with half_pz
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 *improve poem model

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 |-
 |Guli ||
-*read papers about Transfer learning and solving OOV
+* finished the first draft of the survey
-*conducted comparative test
+* voice tagging
-*writing survey
 ||
-* complete the first draft of the survey
+* morpheme-based nmt
 |-
 |Peilun Xiao ||

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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
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-*extract encoder outputs
 |-
 |Shiyue Zhang ||

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 *[[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 and under debugging;
+*finished coding of only adding attention to the decoder and under debugging;
 *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]] ;
 *prepare the dictionary for the memory loading;

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 | rowspan="6"|2016/12/26
 |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]]
+*[[s2smn:]] read six papers to fix the details of our model;
-*wrote part of the code of mn.
+*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]]
+*finished coding and under debugging;
-*[[Huilan:]] fixed the problem of syntax-based translation.
+*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.
+*prepare the dictionary for the memory loading;
 ||
-*[[s2smn:]] finish the code of adding mn.
+*[[s2smn:]] run the ecperiments.
-*[[Huilan:]] handover.
+*[[rnng+mn:]] try to find the problem.
 |-
 |Jiyuan Zhang ||

@@ 第30行： / 第30行： @@
 * tried to change cos to only inner product, and inner product is better than cos.
 * [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/9f/RNNG%2Bmm_experiment_report.pdf report]]
-* read a paper [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/92/DEEP_BIAFFINE_ATTENTION_FOR_NEURAL_DEPENDENCY_PARSING.pdf]]
+* read 3 papers [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/92/DEEP_BIAFFINE_ATTENTION_FOR_NEURAL_DEPENDENCY_PARSING.pdf]] [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/a/aa/Simple_and_Accurate_Dependency_Parsing_Using_Bidirectional_LSTM_Feature_Representations.pdf]] [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/f/fb/Bi-directional_Attention_with_Agreement_for_Dependency_Parsing.pdf]]
 * trying the joint training, which got a problem of optimization.
 ||
 * try the joint training
-* read more papers [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/a/aa/Simple_and_Accurate_Dependency_Parsing_Using_Bidirectional_LSTM_Feature_Representations.pdf]] [[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/f/fb/Bi-directional_Attention_with_Agreement_for_Dependency_Parsing.pdf]]
+* read more papers and write a summary
 |-
 |Guli ||

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 |-
 |Peilun Xiao ||
-*use LDA to generate 10-500 dimension document vector in the rest datasets
+*learned tf-idf algorithm
-*write a python code about a new algorithm about tf-idf
+*coded tf-idf alogrithm in python,but found it not worked well
 ||
-*debug the code
+*use sklearn tfidf to test the dataset
 |}