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第22行: |
第22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 |
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| |Shiyue Zhang || | | |Shiyue Zhang || |
Date |
People |
Last Week |
This Week
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2017/3/27
|
Yang Feng |
- tested for the baseline but cannot get the reasonable result.
- debug the baseline to try to reproduce the good result but failed.
- fixed the problem of nan in alpha-gamma method but the result is not good.
- 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
- decide to use tensorflow or theano
- run experiments based on the chosen platform
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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
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- 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
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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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