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| （2位用户的4个中间修订版本未显示） |
| 第2行： |
第2行： |
| | !Date !! People !! Last Week !! This Week | | !Date !! People !! Last Week !! This Week |
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| − | | rowspan="6"|2017/3/27 | + | | rowspan="6"|2017/4/5 |
| | |Yang Feng || | | |Yang Feng || |
| − | *tested for the baseline but cannot get the reasonable result. | + | * Got the sampled 100w good data and ran Moses (BLEU: 30.6) |
| − | *debug the baseline to try to reproduce the good result but failed. | + | * Reimplemented the idea of ACL (added some optimization to the previous code) and check the performance in the following gradual steps: 1. use s_i-1 as memory query; 2. use s_i-1+c_i as memory query; 3. use y as the memory states for attention; 4. use y + smt_attentions * h as memory states for attention. |
| − | *fixed the problem of nan in alpha-gamma method but the result is not good.
| + | * ran experiments for the above steps but the loss was inf. I am looking for reasons. |
| − | *changed the calculation of probability for alpha-gamma method but the result is neither good. | + | |
| − | *ran Moses for cwmt zh-en translation, but the training data is case-sensitive, so need to rerun. | + | |
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| − | *rerun Moses for cwmt zh-en and cs-en | + | *do experiments and write the paper |
| − | *decide to use tensorflow or theano
| + | |
| − | *run experiments based on the chosen platform
| + | |
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| | |Jiyuan Zhang || | | |Jiyuan Zhang || |
| 第22行： |
第18行： |
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| | |Andi Zhang || | | |Andi Zhang || |
| − | *fixed the bug, turns out it rises from the unfamiliarity with numpy.resize() function | + | *revise the original oov model so that it can automatically detect oov words and translate them |
| − | *the demo model can deal with oov problem(both source word and target word are oov) | + | *deal with the situation that source word is oov but target word is not oov first |
| | + | *it didn't predict right |
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| − | *some paper work about graduation design | + | *make the model work as what we wanted |
| − | *run some experiments using theano on old data set and new zh2en from lihang | + | *deal with the situation that source word is oov and target word is also oov, then other situations |
| | |- | | |- |
| | |Shiyue Zhang || | | |Shiyue Zhang || |
| Date |
People |
Last Week |
This Week
|
| 2017/4/5
|
Yang Feng |
- Got the sampled 100w good data and ran Moses (BLEU: 30.6)
- Reimplemented the idea of ACL (added some optimization to the previous code) and check the performance in the following gradual steps: 1. use s_i-1 as memory query; 2. use s_i-1+c_i as memory query; 3. use y as the memory states for attention; 4. use y + smt_attentions * h as memory states for attention.
- ran experiments for the above steps but the loss was inf. I am looking for reasons.
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- do experiments and write the paper
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| Jiyuan Zhang |
- I did keyword expansion on the qx's model
- fixed some bugs
- read two papers
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- improve the effect of the qx's model
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| Andi Zhang |
- revise the original oov model so that it can automatically detect oov words and translate them
- deal with the situation that source word is oov but target word is not oov first
- it didn't predict right
|
- make the model work as what we wanted
- deal with the situation that source word is oov and target word is also oov, then other situations
|
| Shiyue Zhang |
- got a reasonable baseline on big zhen data
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- implement mem model on this baseline, and test on big data
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| Peilun Xiao |
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