cslt Wiki - 用户贡献 [zh-cn]

Cslt-member-visitors

2017-09-14T02:46:35Z

Guojiayu：/* Jiayu Guo (郭佳雨) */

==Professionals==

==Engineers==

=== Yuxin Zhang (张雨心） ===
[[文件:Zyx.jpg|200px]]
* Haixia research center
* 2016.10 -
* Finance processing
* [[媒体文件:Agreement zyx.jpg|Data Security Agreement]]

==Students==

===Jiyuan Zhang (张记袁）===
[[文件:Zhangjiyuan.png|200px]]
* PKU
* 2016.4-
* neural generation model
* [[媒体文件:An overview of machine translation.pptx|Bi-weekly report]]
* [[媒体文件:Zhangjy_data.jpg|Data_security_agreement]]

===Ying Shi (石颖）===
[[文件:Ying_shi.jpg|200px]]
* BJTU
* 2016.6.15-
* Speech processing
* [[媒体文件:Shiying_bi_weekly_report.ppt|Bi-weekly report]]
*[[媒体文件:DataSecurityAgreement_YingShi.jpg|DataSecurityAgreement]]

===Yixiang Chen (陈怿翔)===
[[文件:Chenyx.jpg|200px]]
*University of China Mining
* 2016.7-
* Speech processing
* [[媒体文件:Chenyx_report.pdf |Bi-weekly report]]
* [[媒体文件:Chenyx data.jpg|Data_security_agreement]]

===Shiyue Zhang(张诗悦)===
[[文件:Zhang Shiyue.jpg|200px]]
* BUTP
* 2016.9.06-
* Language processing
* [[媒体文件:1.pic hd.jpg| Data_security_agreement]]

=== Yang Wei (魏扬） ===
[[文件:Weiy_photo.jpg|200px]]
* BUPT
* 2016.10 -
* Speech processing
* [[媒体文件:Bi-monthly report weiy.pdf|Bi-monthly report]]
* [[媒体文件:Data agreement weiy.jpg|Data security agreement]]

===Yanqing Wang（王延清）===
[[文件:wyq photo.jpeg|200px]]
* BUPT
* 2016.11-2017.2
* Speech processing
* [[媒体文件:Bi-weekly_report.pptx|Bi-weekly report]]
*[[媒体文件:DataSecurityAgreement wangyanqing.jpg|Data_security_agreement]]

=== Yaodong Wang (王耀东) ===
[[文件:wangyd.jpg|200px]]
* CUFE
* 2016.12.22 -
* [[媒体文件:Bi_weekly_report.pptx |Bi-weekly report]]
* [[媒体文件:Data_security_Agreement.jpg|Data security agreement]]
* Financial processing

=== Tongzheng Ren (任桐正) ===
[[文件:IcCardPicture.do2.jpeg|200px]]
* THU
* 2016.12.22 -
* [[媒体文件:利用LSTM预测时间序列.pptx|Bi-weekly report]]
* [[媒体文件:Data Security Agreement-Tongzheng Ren.jpg|Data security agreement]]
* Financial processing

=== Shipan Ren (任师攀) ===
[[文件:Rsp.jpg|200px]]
* PKU
* 2017.05.10 -
* [[媒体文件:seq2seq.pptx|Bi-weekly report]]
* [[媒体文件:Agreement.jpg|Data security agreement]]
* Language processing

=== Miao Zhang (张淼) ===
[[文件:miao.JPG|200px]]
* BUPT
* 2017.5.1 -
* [[媒体文件:Zm cough.pdf |Bi-weekly report]]
* [[媒体文件:Zm.JPG|Data security agreement]]
* Speech processing

=== Xuejing Zhang (张学敬) ===
[[文件:Zhangxuejing.jpg|200px]]
* BISTU
* 2017.7.7 -
* [[媒体文件:Zhangxj.jpg|Data security agreement]]
* Language processing

=== Xiaofei Kang (康晓非) ===
[[文件:头像.jpg|200px]]
* PKU
* 2017.7.17 -
* [[媒体文件:xxxx.jpg|Bi-weekly report]]
* [[媒体文件:Kangxf_Data.jpg|Data security agreement]]
* Speech processing

=== Jiayu Guo (郭佳雨) ===
[[文件:1.jpg|200px]]
* PKU
* 2017.7.18 -
* [[媒体文件:security.jpg|Data security agreement]]
* Natural Laguage Processing

文件:Security.jpg

2017-09-14T02:46:02Z

Guojiayu：Guojiayu上传“文件:Security.jpg”的新版本

文件:Security.jpg

2017-09-14T02:45:58Z

Guojiayu：Guojiayu上传“文件:Security.jpg”的新版本

文件:1.jpg

2017-09-14T02:34:19Z

Guojiayu：Guojiayu上传“文件:1.jpg”的新版本

2017-08-21T03:27:48Z

Guojiayu：

| class="wikitable"
!Date!!People !! Last Week !! This Week
|-
| rowspan="7"|2017.8.21

|-
|guojiayu
||
*
||
* read seq2seq_model source code;
|-

2017-08-12T14:35:56Z

Guojiayu：/* Daily Report */

=NLP Schedule=

==Members==

===Current Members===

* Yang Feng (冯洋)
* Jiyuan Zhang （张记袁）
* Aodong Li (李傲冬)
* Andi Zhang (张安迪)
* Shiyue Zhang (张诗悦)
* Li Gu (古丽)
* Peilun Xiao (肖培伦)
* Shipan Ren (任师攀)
* Jiayu Guo (郭佳雨)

===Former Members===
* '''Chao Xing (邢超)''' : FreeNeb
* '''Rong Liu (刘荣)''' : 优酷
* '''Xiaoxi Wang (王晓曦)''' : 图灵机器人
* '''Xi Ma (马习)''' : 清华大学研究生
* '''Tianyi Luo (骆天一)''' ： phd candidate in University of California Santa Cruz
* '''Qixin Wang (王琪鑫)''' : MA candidate in University of California
* '''DongXu Zhang (张东旭)''': --
* '''Yiqiao Pan (潘一桥)''' ： MA candidate in University of Sydney
* '''Shiyao Li （李诗瑶）''' : BUPT
* '''Aiting Liu (刘艾婷)''' : BUPT

==Work Progress==
===Daily Report===

{|class="wikitable"
! Date !! Person !! start!! leave !! hours ||status
|-
| rowspan="2"|2017/04/02
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/03
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/04
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/05
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/06
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/07
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/08
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/09
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/10
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/11
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/12
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/13
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/14
|Andy Zhang||9:30 ||18:30 ||8 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="2"|2017/04/15
|Andy Zhang||9:00 ||15:00 ||6 ||
*preparing EMNLP
|-
|Peilun Xiao || || || ||
|-
| rowspan="1"|2017/04/18
|Aodong Li||11:00 ||20:00 ||8 ||
*Pick up new task in news generation and do literature review
|-
| rowspan="1"|2017/04/19
|Aodong Li||11:00 ||20:00 ||8 ||
*Literature review
|-
| rowspan="1"|2017/04/20
|Aodong Li||12:00 ||20:00 ||8 ||
*Literature review
|-
| rowspan="1"|2017/04/21
|Aodong Li||12:00 ||20:00 ||8 ||
*Literature review
|-
| rowspan="1"|2017/04/24
|Aodong Li||11:00 ||20:00 ||8 ||
*Adjust literature review focus
|-
| rowspan="1"|2017/04/25
|Aodong Li||11:00 ||20:00 ||8 ||
*Literature review
|-
| rowspan="1"|2017/04/26
|Aodong Li||11:00 ||20:00 ||8 ||
*Literature review
|-
| rowspan="1"|2017/04/27
|Aodong Li||11:00 ||20:00 ||8 ||
*Try to reproduce sc-lstm work
|-
| rowspan="1"|2017/04/28
|Aodong Li||11:00 ||20:00 ||8 ||
*Transfer to new task in machine translation and do literature review
|-
| rowspan="1"|2017/04/30
|Aodong Li||11:00 ||20:00 ||8 ||
*Literature review
|-
| rowspan="1"|2017/05/01
|Aodong Li||11:00 ||20:00 ||8 ||
*Literature review
|-
| rowspan="1"|2017/05/02
|Aodong Li||11:00 ||20:00 ||8 ||
*Literature review and code review
|-
| rowspan="1"|2017/05/06
|Aodong Li||14:20 ||17:20||3 ||
*Code review
|-
| rowspan="1"|2017/05/07
|Aodong Li||13:30 ||22:00||8 ||
*Code review and experiment started, but version discrepancy encountered
|-
| rowspan="1"|2017/05/08
|Aodong Li||11:30 ||21:00 ||8 ||
*Code review and version discrepancy solved
|-
| rowspan="1"|2017/05/09
|Aodong Li||13:00 ||22:00 ||9 ||
*Code review and experiment
*details about experiment:
small data,
1st and 2nd translator uses the same training data,
2nd translator uses '''random initialized embedding'''
*results (BLEU):
BASELINE: 43.87
best result of our model: 42.56
|-
| rowspan="1"|2017/05/10
|Shipan Ren || 9:00 || 20:00 || 11 ||
*Entry procedures
*Machine Translation paper reading
|-
| rowspan="1"|2017/05/10
|Aodong Li || 13:30 || 22:00 || 8 ||
*experiment setting:
small data,
1st and 2nd translator uses the different training data, counting 22000 and 22017 seperately
2nd translator uses '''random initialized embedding'''
*results (BLEU):
BASELINE: 36.67 (36.67 is the model at 4750 updates, but we use model at 3000 updates to
prevent the case of overfitting, to generate the 2nd translator's training data, for
which the BLEU is 34.96)
best result of our model: 29.81
This may suggest that that using either the same training data with 1st translator or different
one won't influence 2nd translator's performance, instead, using the same one may
be better, at least from results. But I have to give a consideration of a smaller size
of training data compared to yesterday's model.
*code 2nd translator with constant embedding
|-
| rowspan="1"|2017/05/11
|Shipan Ren || 10:00 || 19:30 || 9.5 ||
*Configure environment
*Run tf_translate code
*Read Machine Translation paper
|-
| rowspan="1"|2017/05/11
|Aodong Li || 13:00 || 21:00|| 8 ||
*experiment setting:
small data,
1st and 2nd translator uses the same training data,
2nd translator uses '''constant untrainable embedding''' imported from 1st translator's decoder
*results (BLEU):
BASELINE: 43.87
best result of our model: 43.48
Experiments show that this kind of series or cascade model will definitely impair the final perfor-
mance due to information loss as the information flows through the network from
end to end. Decoder's smaller vocabulary size compared to encoder's demonstrate
this (9000+ -> 6000+).
The intention of this experiment is looking for a map to solve meaning shift using 2nd translator,
but result of whether the map is learned or not is obscured by the smaller vocab size
phenomenon.
*literature review on hierarchical machine translation
|-
| rowspan="1"|2017/05/12
|Aodong Li||13:00 ||21:00 ||8 ||
*Code double decoding model and read multilingual MT paper
|-
| rowspan="1"|2017/05/13
|Shipan Ren || 10:00 || 19:00 || 9 ||
*read machine translation paper
*learne lstm model and seq2seq model
|-
| rowspan="1"|2017/05/14
|Aodong Li || 10:00 || 20:00 || 9 ||
*Code double decoding model and experiment
*details about experiment:
small data,
2nd translator uses as training data the concat(Chinese, machine translated English),
2nd translator uses '''random initialized embedding'''
*results (BLEU):
BASELINE: 43.87
best result of our model: 43.53
*NEXT: 2nd translator uses '''trained constant embedding'''
|-
| rowspan="1"|2017/05/15
|Shipan Ren || 9:30 || 19:00 || 9.5 ||
* understand the difference between lstm model and gru model
* read the implement code of seq2seq model
|-
| rowspan="2"|2017/05/17
|Shipan Ren || 9:30 || 19:30 || 10 ||
* read neural machine translation paper
* read tf_translate code
|-
|Aodong Li || 13:30 || 24:00 || 9||
* code and debug double-decoder model
* alter 2017/05/14 model's size and will try after nips
|-
| rowspan="2"|2017/05/18
|Shipan Ren || 10:00 || 19:00 || 9 ||
* read neural machine translation paper
* read tf_translate code
|-
|Aodong Li || 12:30 || 21:00 || 8 ||
* train double-decoder model on small data set but encounter decode bugs
|-
| rowspan="1"|2017/05/19
|Aodong Li || 12:30 || 20:30 || 8 ||
* debug double-decoder model
* the model performs well on develop set, but performs badly on test data. I want to figure out the reason.
|-
| rowspan="1"|2017/05/21
|Aodong Li || 10:30 || 18:30 || 8 ||
*details about experiment:
hidden_size = 700 (500 in prior)
emb_size = 510 (310 in prior)
small data,
2nd translator uses as training data the concat(Chinese, machine translated English),
2nd translator uses '''random initialized embedding'''
*results (BLEU):
BASELINE: 43.87
best result of our model: '''45.21'''
But only one checkpoint outperforms the baseline, the other results are commonly under 43.1
* debug double-decoder model
|-
| rowspan="1"|2017/05/22
|Aodong Li || 14:00 || 22:00 || 8 ||
*double-decoder without joint loss generalizes very bad
*i'm trying double-decoder model with joint loss
|-
| rowspan="1"|2017/05/23
|Aodong Li || 13:00 || 21:30 || 8 ||
*details about experiment 1:
hidden_size = 700
emb_size = 510
learning_rate = 0.0005 (0.001 in prior)
small data,
2nd translator uses as training data the concat(Chinese, machine translated English),
2nd translator uses '''random initialized embedding'''
*results (BLEU):
BASELINE: 43.87
best result of our model: '''42.19'''
Overfitting? In overall, the 2nd translator performs worse than baseline
*details about experiment 2:
hidden_size = 500
emb_size = 310
learning_rate = 0.001
small data,
double-decoder model with joint loss which means the final loss = 1st decoder's loss + 2nd
decoder's loss
*results (BLEU):
BASELINE: 43.87
best result of our model: '''39.04'''
The 1st decoder's output is generally better than 2nd decoder's output. The reason may be that
the second decoder only learns from the first decoder's hidden states because their states are
almost the same.
*DISCOVERY:
The reason why double-decoder without joint loss generalizes very bad is that the gap between
force teaching mechanism (training process) and beam search mechanism (decoding process)
propagates and expands the error to the output end, which destroys the model when decoding.
*next:
Try to train double-decoder model without joint loss but with beam search on 1st decoder.
|-
| rowspan="1"|2017/05/24
|Aodong Li || 13:00 || 21:30 || 8 ||
*code double-attention one-decoder model
*code double-decoder model
|-

| rowspan="1"|2017/05/24
|Shipan Ren || 10:00 || 20:00 || 10 ||
*read neural machine translation paper
*read tf_translate code
|-

| rowspan="2"|2017/05/25
|Shipan Ren || 9:30 || 18:30 || 9 ||
*write document of tf_translate project
*read neural machine translation paper
*read tf_translate code
|-
|Aodong Li || 13:00 || 22:00 || 9 ||
* code and debug double attention model
|-

| rowspan="1"|2017/05/27
|Shipan Ren || 9:30 || 18:30 || 9 ||
*read tf_translate code
*write document of tf_translate project
|-
| rowspan="1"|2017/05/28
|Aodong Li || 15:00 || 22:00 || 7 ||
*details about experiment:
hidden_size = 500
emb_size = 310
learning_rate = 0.001
small data,
2nd translator uses as training data both Chinese and machine translated English
Chinese and English use different encoders and different attention
'''final_attn = attn_1 + attn_2'''
2nd translator uses '''random initialized embedding'''
*results (BLEU):
BASELINE: 43.87
when decoding:
final_attn = attn_1 + attn_2 best result of our model: '''43.50'''
final_attn = 2/3attn_1 + 4/3attn_2 best result of our model: '''41.22'''
final_attn = 4/3attn_1 + 2/3attn_2 best result of our model: '''43.58'''
|-
| rowspan="1"|2017/05/30
|Aodong Li || 15:00 || 21:00 || 6 ||
*details about experiment 1:
hidden_size = 500
emb_size = 310
learning_rate = 0.001
small data,
2nd translator uses as training data both Chinese and machine translated English
Chinese and English use different encoders and different attention
'''final_attn = 2/3attn_1 + 4/3attn_2'''
2nd translator uses '''random initialized embedding'''
*results (BLEU):
BASELINE: 43.87
best result of our model: '''42.36'''
* details about experiment 2:
'''final_attn = 2/3attn_1 + 4/3attn_2'''
2nd translator uses '''constant initialized embedding'''
*results (BLEU):
BASELINE: 43.87
best result of our model: '''45.32'''
* details about experiment 3:
'''final_attn = attn_1 + attn_2'''
2nd translator uses '''constant initialized embedding'''
*results (BLEU):
BASELINE: 43.87
best result of our model: '''45.41''' and it seems more stable
|-
| rowspan="2"|2017/05/31
|Shipan Ren || 10:00 || 19:30 || 9.5 ||
*run and test tf_translate code
*write document of tf_translate project
|-
|Aodong Li || 12:00 || 20:30 || 8.5 ||
* details about experiment 1:
'''final_attn = 4/3attn_1 + 2/3attn_2'''
2nd translator uses '''constant initialized embedding'''
*results (BLEU):
BASELINE: 43.87
best result of our model: '''45.79'''
* That only make English word embedding at encoder constant and train all the other embedding and parameters achieves an even higher bleu score 45.98 and the results are stable.
* The quality of English embedding at encoder plays an pivotal role in this model.
* Preparation of big data.
|-
| rowspan="1"|2017/06/01
|Aodong Li || 13:00 || 24:00 || 11 ||
* Only make the English encoder's embedding constant -- 45.98
* Only initialize the English encoder's embedding and then finetune it -- 46.06
* Share the attention mechanism and then directly add them -- 46.20
* Run double-attention model on large data
|-
| rowspan="1"|2017/06/02
|Aodong Li || 13:00 || 22:00 || 9 ||
* Baseline bleu on large data is 30.83 with '''30000''' output vocab
* Our best result is 31.53 with '''20000''' output vocab
|-
| rowspan="1"|2017/06/03
|Aodong Li || 13:00 || 21:00 || 8 ||
* Train the model with 40 batch size and with concat(attn_1, attn_2)
* the best result of model with 40 batch size and with add(attn_1, attn_2) is 30.52
|-
| rowspan="1"|2017/06/05
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
|-
| rowspan="1"|2017/06/06
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
|-
| rowspan="1"|2017/06/07
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
|-
| rowspan="1"|2017/06/08
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
|-
| rowspan="1"|2017/06/09
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
|-
| rowspan="1"|2017/06/12
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
|-
| rowspan="1"|2017/06/13
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
|-
| rowspan="1"|2017/06/14
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
|-
| rowspan="1"|2017/06/15
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
* Read paper about MT involving grammar
|-
| rowspan="1"|2017/06/16
|Aodong Li || 10:00 || 19:00 || 8 ||
* Prepare for APSIPA paper
* Read paper about MT involving grammar
|-
| rowspan="1"|2017/06/19
|Aodong Li || 10:00 || 19:00 || 8 ||
* Completed APSIPA paper
* Took new task in style translation
|-
| rowspan="1"|2017/06/20
|Aodong Li || 10:00 || 19:00 || 8 ||
* Tried synonyms substitution
|-
| rowspan="1"|2017/06/21
|Aodong Li || 10:00 || 19:00 || 8 ||
* Tried post edit like synonyms substitution but this didn't work
|-
| rowspan="1"|2017/06/22
|Aodong Li || 10:00 || 19:00 || 8 ||
* Trained a GRU language model to determine similar word
|-
| rowspan="2"|2017/06/23
|Shipan Ren || 10:00 || 21:00 || 11 ||
* read neural machine translation paper
* read and run tf_translate code
|-
|Aodong Li || 10:00 || 19:00 || 8 ||
* Trained a GRU language model to determine similar word
* This didn't work because semantics is not captured
|-
| rowspan="2"|2017/06/26
|Shipan Ren || 10:00 || 21:00 || 11 ||
* read paper：LSTM Neural Networks for Language Modeling
* read and run ViVi_NMT code
|-
|Aodong Li || 10:00 || 19:00 || 8 ||
* Tried to figure out new ways to change the text style
|-
| rowspan="2"|2017/06/27
|Shipan Ren || 10:00 || 20:00 || 10 ||
* read the API of tensorflow
* debugged ViVi_NMT and tried to upgrade code version to tensorflow1.0
|-
|Aodong Li || 10:00 || 19:00 || 8 ||
* Trained seq2seq model to solve this problem
* Semantics are stored in fixed-length vectors by a encoder and a decoder generate sequences on this vector
|-
| rowspan="2"|2017/06/28
|Shipan Ren || 10:00 || 19:00 || 9 ||
* debugged ViVi_NMT and tried to upgrade code version to tensorflow1.0 (on server)
* installed tensorflow0.1 and tensorflow1.0 on my pc and debugged ViVi_NMT
|-
|Aodong Li || 10:00 || 19:00 || 8 ||
* Cross-domain seq2seq w/o attention and w/ attention models didn't work because of overfitting
|-
| rowspan="2"|2017/06/29
|Shipan Ren || 10:00 || 20:00 || 10 ||
* read the API of tensorflow
* debugged ViVi_NMT and tried to upgrade code version to tensorflow1.0 (on server)
|-
|Aodong Li || 10:00 || 19:00 || 8 ||
* Read style transfer papers
|-
| rowspan="2"|2017/06/30
|Shipan Ren || 10:00 || 24:00 || 14 ||
* debugged ViVi_NMT and tried to upgrade code version to tensorflow1.0 (on server)
* accomplished this task
* found the new version saves more time，has lower complexity and better bleu than before
|-
|Aodong Li || 10:00 || 19:00 || 8 ||
* Read style transfer papers
|-
| rowspan="1"|2017/07/03
|Shipan Ren || 9:00 || 21:00 || 12 ||
* run two versions of the code on small data sets (Chinese-English)
* tested these checkpoint

|-
| rowspan="1"|2017/07/04
|Shipan Ren || 9:00 || 21:00 || 12 ||
* recorded experimental results
* found version 1.0 of the code save more training time, has less complexity and these two version of the code has a similar Bleu value
* found that the Bleu is still good when the model is over fitting
* reason: the test set and training set are similar in content and style on small data set

|-
| rowspan="1"|2017/07/05
|Shipan Ren || 9:00 || 21:00 || 12 ||
* run two versions of the code on big data sets (Chinese-English)
* read NMT papers

|-
| rowspan="1"|2017/07/06
|Shipan Ren || 9:00 || 21:00 || 12 ||
* out of memory（OOM） error occurred when version 0.1 of code was trained using large data set，but version 1.0 worked
* reason: improper distribution of resources by the tensorflow0.1 version leads to exhaustion of memory resources
* I've tried many times, and version 0.1 worked
|-
| rowspan="1"|2017/07/07
|Shipan Ren || 9:00 || 21:00 || 12 ||
* tested these checkpoints and recorded experimental results
* the version 1.0 code saved 0.06 second per step than the version 0.1 code
|-
| rowspan="1"|2017/07/08
|Shipan Ren || 9:00 || 21:00 || 12 ||
* downloaded the wmt2014 data set
* used the English-French data set to run the code and found the translation is not good
* reason:no data preprocessing is done

|-

| rowspan="1"|2017/07/18
|Jiayu Guo || 8:30|| 22:00 || 14 ||
* read model code.
|-
| rowspan="1"|2017/07/19
|Jiayu Guo || 9:00|| 22:00 || 13 ||
* read papers of bleu.
|-
| rowspan="1"|2017/07/20
|Jiayu Guo || 9:00|| 22:00 || 13 ||
* read papers of attention mechanism.
|-

| rowspan="1"|2017/07/21
|Jiayu Guo || 10:00|| 23:00 || 13 ||
* process document
|-
| rowspan="1"|2017/07/24
|Jiayu Guo || 9:00|| 22:00 || 13 ||
* read model code.
|-

| rowspan="1"|2017/07/25
|Jiayu Guo || 9:00|| 23:00 || 14 ||
* process document
|-
| rowspan="1"|2017/07/26
|Jiayu Guo || 10:00|| 24:00 || 14 ||
* process document
|-
| rowspan="1"|2017/07/27
|Jiayu Guo || 10:00|| 24:00 || 14 ||
* process document
|-
| rowspan="1"|2017/07/28
|Jiayu Guo || 9:00|| 24:00 || 15 ||
* process document

|
|-
| rowspan="1"|2017/07/31
|Jiayu Guo || 10:00|| 23:00 || 13 ||
* split ancient language text to single word
|
|-
| rowspan="1"|2017/08/1
|Jiayu Guo || 10:00|| 23:00 || 13 ||
* run seq2seq_model
|
|-
| rowspan="1"|2017/08/2
|Jiayu Guo || 10:00|| 23:00 || 13 ||
* process document
|-
| rowspan="1"|2017/08/3
|Jiayu Guo || 10:00|| 23:00 || 13 ||
* process document
|-
| rowspan="1"|2017/08/4
|Jiayu Guo || 10:00|| 23:00 || 13 ||
* search new data(Songshu)
|-
| rowspan="1"|2017/08/7
|Jiayu Guo || 9:00|| 22:00 || 13 ||
* process document
|-
| rowspan="1"|2017/08/8
|Jiayu Guo || 10:00|| 21:00 || 11 ||
* read tensorflow
|-
| rowspan="1"|2017/08/9
|Jiayu Guo || 10:00|| 23:00 || 13 ||
* run model with the data of which ancient content was split by single character.
|-

| rowspan="1"|2017/08/10
|Jiayu Guo || 9:00|| 23:00 || 13 ||
* process data of Songshu
* read papers of CNN
|-
| rowspan="1"|2017/08/11
|Jiayu Guo || 10:00|| 23:00 || 13 ||
* learn about Graphic Model of LSTM-Projected BPTT
* search for data available for translation (Twenty-four-Shi)
|-s
| rowspan="1"|2017/08/12
|Jiayu Guo || 11:00|| 23:30 || 12 ||
* run model with data including Shiji、Zizhitongjian.
|-
}

===Time Off Table===

{| class="wikitable"
! Date !! Yang Feng !! Jiyuan Zhang
|-
|}

==Past progress==
[[nlp-progress 2017/03]]

[[nlp-progress 2017/02]]

[[nlp-progress 2017/01]]

[[nlp-progress 2016/12]]

[[nlp-progress 2016/11]]

[[nlp-progress 2016/10]]

[[nlp-progress 2016/09]]

[[nlp-progress 2016/08]]

[[nlp-progress 2016/05/01 -- 08/16 | nlp-progress 2016/05-07]]

[[nlp-progress 2016/04]]

Schedule

2017-08-12T14:31:28Z

Guojiayu：/* Daily Report */

Schedule

2017-08-10T01:52:08Z

Guojiayu：/* Daily Report */

Schedule

2017-08-10T01:44:22Z

Guojiayu：/* Daily Report */

Schedule

2017-08-10T01:29:07Z

Guojiayu：/* Daily Report */