“Tianyi Luo 2015-12-28”版本间的差异
来自cslt Wiki
| （相同用户的8个中间修订版本未显示） | |||
| 第1行： | 第1行： | ||
=== Plan to do next week === | === Plan to do next week === | ||
| − | + | * To finish the work about making the lab's demo. | |
| − | * To | + | |
* To try new kernel function to model candidate similarity more efficiently. | * To try new kernel function to model candidate similarity more efficiently. | ||
=== Work done in this week === | === Work done in this week === | ||
| − | * Finish some parts of work about | + | * Finish some parts of work about making the lab's demo. |
| − | * Finish the work about local-based attention Chinese couplet generation. | + | * Finish the work about local-based attention Chinese couplet generation model. |
开 业 大 吉： | 开 业 大 吉： | ||
| 第29行： | 第28行： | ||
=== Plan to do next week === | === Plan to do next week === | ||
| − | * To finish the work about | + | * To finish the work about making the lab's demo. |
| − | * To finish the work about the | + | * To finish the work about the SMT method implementation of the poem generation. |
| − | * To tackle the problem of attention-based | + | * To tackle the problem of attention-based programe. |
| + | * To implement the reading comprehension qa system. | ||
| + | * To extract the SMT features to enhance the function of poem generation and songci generation. | ||
===Interested papers === | ===Interested papers === | ||
*Cascading Bandits: Learning to Rank in the Cascade Model(ICML 2015) [[http://zheng-wen.com/Cascading_Bandit_Paper.pdf pdf]] | *Cascading Bandits: Learning to Rank in the Cascade Model(ICML 2015) [[http://zheng-wen.com/Cascading_Bandit_Paper.pdf pdf]] | ||
| + | * Neural Machine Translation by Joint Learning to Align and Translate(ICLR 2015)[[http://xueshu.baidu.com/s?wd=paperuri%3A%283242f94dcbfc892f63c9f51acd8ef8ce%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fabs%2F1409.0473&ie=utf-8 pdf]] | ||
2015年12月31日 (四) 05:11的最后版本
Plan to do next week
- To finish the work about making the lab's demo.
- To try new kernel function to model candidate similarity more efficiently.
Work done in this week
- Finish some parts of work about making the lab's demo.
- Finish the work about local-based attention Chinese couplet generation model.
开 业 大 吉：
同 行 增 劲 旅：
training corpus:同 行 增 劲 旅 / 商 界 跃 新 军 / ;
test result:
Non-local attention-based:
[ 0.15731922 0.15440576 0.154654 0.13509884 0.13408586 0.13055836 0.13387793] [ 0.15748511 0.15446058 0.15466693 0.13504058 0.1340386 0.13050689 0.13380134] [ 0.15726063 0.15442531 0.15467082 0.13510644 0.13408728 0.13055961 0.1338899 ] [ 0.15715003 0.15439823 0.15466341 0.13514642 0.13413033 0.13059665 0.13391495] [ 0.15717115 0.15440425 0.15468264 0.13513321 0.13412073 0.13058177 0.13390623]
同 行 增 劲 旅 / 春 风 送 四 季 /
Local attention-based:
同 行 增 劲 旅 / 人 情 安 四 春 /
Plan to do next week
- To finish the work about making the lab's demo.
- To finish the work about the SMT method implementation of the poem generation.
- To tackle the problem of attention-based programe.
- To implement the reading comprehension qa system.
- To extract the SMT features to enhance the function of poem generation and songci generation.
