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| 第38行： |
第38行： |
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| | |Guli || | | |Guli || |
| − | *read papers about Transfer learning and solving OOV | + | * finished the first draft of the survey |
| − | *conducted comparative test | + | * voice tagging |
| − | *writing survey
| + | |
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| − | * complete the first draft of the survey | + | * morpheme-based nmt |
| | + | * improve nmt with monolingual data |
| | |- | | |- |
| | |Peilun Xiao || | | |Peilun Xiao || |
| Date |
People |
Last Week |
This Week
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| 2016/12/26
|
Yang Feng |
- 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 of only adding attention to the decoder and under debugging;
- refine Moses manual [manual] ;
- prepare the dictionary for the memory loading;
- Huilan: documentation
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|
| Jiyuan Zhang |
- coded tone_model,but had some trouble
- run global_attention_model that decodes four sentences, fourfivegenerated by local_attention model
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|
| Andi Zhang |
- coded to output encoder outputs and correspoding source & target sentences(ids in dictionaries)
- 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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| Shiyue Zhang |
- tried to add true action info when training gate, which got better results than no true actions, but still not very good.
- tried different scale vectors, and found setting >=-5000 is good
- tried to change cos to only inner product, and inner product is better than cos.
- [report]
- read 3 papers [[1]] [[2]] [[3]]
- trying the joint training, which got a problem of optimization.
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- try the joint training
- read more papers and write a summary
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| Guli |
- finished the first draft of the survey
- voice tagging
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- morpheme-based nmt
- improve nmt with monolingual data
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| 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
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- use sklearn tfidf to test the dataset
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