“Tianyi Luo 2015-12-21”版本间的差异
| （相同用户的7个中间修订版本未显示） | |||
| 第18行： | 第18行： | ||
| − | + | 同 行 增 劲 旅： | |
| + | |||
| + | training corpus:同 行 增 劲 旅 / 商 界 跃 新 军 / ; 上 沃 群 芳 艳 / 国 宁 百 艺 生 / | ||
| + | |||
| + | test result: | ||
Non-local attention-based: | Non-local attention-based: | ||
| − | + | attention of 商 [ 0.00851025, 0.05046642, 0.20085089, 0.52851975, 0.12252463, 0.06678692, 0.02234111] | |
| + | |||
| + | attention of 军 [ 0.00760446, 0.04773411, 0.20061702, 0.54270059, 0.11813291, 0.06291854, 0.02029242] | ||
| + | |||
| + | attention of 胜 [ 0.00872168, 0.05112754, 0.20125151, 0.52559483, 0.12306171, 0.06754488, 0.02269783] | ||
| + | |||
| + | attention of 旧 [ 0.00775181, 0.04831868, 0.20060426, 0.54085833, 0.11861438, 0.06334573, 0.02050681] | ||
| + | |||
| + | attention of 来 [ 0.0080967, 0.04938305, 0.20097148, 0.53481424, 0.12035656, 0.06504875, 0.02132925] | ||
| + | |||
| + | 同 行 增 劲 旅 / 商 军 胜 旧 来 / | ||
Local attention-based: | Local attention-based: | ||
| − | + | 同 行 增 劲 旅 / 商 宁 百 艺 生 / | |
=== Plan to do next week === | === Plan to do next week === | ||
* To finish the work about make the lab's demo. | * To finish the work about make the lab's demo. | ||
* To finish the work about the poem and couplet generation's SMT method. | * To finish the work about the poem and couplet generation's SMT method. | ||
| + | |||
| + | ===Interested papers === | ||
| + | *Cascading Bandits: Learning to Rank in the Cascade Model(ICML 2015) [[http://zheng-wen.com/Cascading_Bandit_Paper.pdf pdf]] | ||
2015年12月25日 (五) 08:18的最后版本
Plan to do next week
- Enhance the function of couplet generation function.
- To conduct the experiments to submit a journal.
- To try new kernel function to model candidate similarity more efficiently.
Work done in this week
- Finish some parts of work about make the lab's demo.
- Finish some parts of work about the poem and couplet generation's SMT method.
- Finish the work about local-based attention Chinese couplet generation.
开 业 大 吉：
Non-local attention-based:
启 步 肇 昌 隆 / 花 荣 名 畅 三 /
Local attention-based:
妙 墨 系 春 秋 / 名 来 昌 畅 花 /
同 行 增 劲 旅：
training corpus:同 行 增 劲 旅 / 商 界 跃 新 军 / ; 上 沃 群 芳 艳 / 国 宁 百 艺 生 /
test result:
Non-local attention-based:
attention of 商 [ 0.00851025, 0.05046642, 0.20085089, 0.52851975, 0.12252463, 0.06678692, 0.02234111]
attention of 军 [ 0.00760446, 0.04773411, 0.20061702, 0.54270059, 0.11813291, 0.06291854, 0.02029242]
attention of 胜 [ 0.00872168, 0.05112754, 0.20125151, 0.52559483, 0.12306171, 0.06754488, 0.02269783]
attention of 旧 [ 0.00775181, 0.04831868, 0.20060426, 0.54085833, 0.11861438, 0.06334573, 0.02050681]
attention of 来 [ 0.0080967, 0.04938305, 0.20097148, 0.53481424, 0.12035656, 0.06504875, 0.02132925]
同 行 增 劲 旅 / 商 军 胜 旧 来 /
Local attention-based:
同 行 增 劲 旅 / 商 宁 百 艺 生 /
Plan to do next week
- To finish the work about make the lab's demo.
- To finish the work about the poem and couplet generation's SMT method.
Interested papers
- Cascading Bandits: Learning to Rank in the Cascade Model(ICML 2015) [pdf]
