“Tianyi Luo 2016-04-25”版本间的差异

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(相同用户的4个中间修订版本未显示)
第16行: 第16行:
 
--------------------2016-04-22
 
--------------------2016-04-22
 
* Speed up process of the test performance about theano version of Generationg the similar questions' vectors based on RNN.
 
* Speed up process of the test performance about theano version of Generationg the similar questions' vectors based on RNN.
 +
* Help conduct the presentation of text group(Tang poetry and Songci generation and Intelligent QA system) for Tsinghua University's 105 anniversary.
 
--------------------2016-04-23
 
--------------------2016-04-23
 
* Use entity match rule 1(dpk, ix, lpk sv + machine number) to improve the accuracy from 38% to 58%.
 
* Use entity match rule 1(dpk, ix, lpk sv + machine number) to improve the accuracy from 38% to 58%.
第21行: 第22行:
 
* Use entity match rules 2+3(dps, s, e, fp, tps, q, 200 + machine number and machine number only) to improve the accuracy from 58% to 72.09%.
 
* Use entity match rules 2+3(dps, s, e, fp, tps, q, 200 + machine number and machine number only) to improve the accuracy from 58% to 72.09%.
 
* Use entity match rules 4(dpk, ix, lpk sv, dps, s, e, fp, tps, q + machine number + English chacracters) to improve the accuracy from 72.09% to 72.86%.
 
* Use entity match rules 4(dpk, ix, lpk sv, dps, s, e, fp, tps, q + machine number + English chacracters) to improve the accuracy from 72.09% to 72.86%.
* Use entity match rules 5(machine number + English chacracters) to improve the accuracy from 72.86% to ?%.
+
* Use entity match rules 5(machine number + English chacracters) to improve the accuracy from 72.86% to 76.48%.
 +
* Use simlar pair match rules 1(“快”和“速度”、“复写”和“拷贝”等等) to improve the accuracy from 76.48% to 82.39%.
 
=== Plan to do next week ===
 
=== Plan to do next week ===
 
* To implement tensorflow version of RNN/LSTM Max margin vector training.
 
* To implement tensorflow version of RNN/LSTM Max margin vector training.

2016年4月25日 (一) 00:36的最后版本

Plan to do this week

  • To implement tensorflow version of RNN/LSTM Max margin vector training.

Work done in this week


2016-04-18
  • Optimize theano version of Generationg the similar questions' vectors based on RNN.
  • Finish implementing theano version of LSTM Max margin vector training.

2016-04-19
  • Optimize theano version of Generationg the similar questions' vectors based on RNN.

2016-04-20
  • Finish submiting the camera version paper of IJCAI 2016.
  • Update the version of Technical Report about Chinese Song Iambics generation.

2016-04-21
  • Finish helping Teacher Wang to prepare for text group's presentation(Tang poetry and Songci generation and Intelligent QA system) for Tsinghua University's 105 anniversary.
  • Submit our IJCAI paper to arxiv. (Solve a big problem about submitting the paper including Chinese chacracters. Solution)
  • Optimize theano version of Generationg the similar questions' vectors based on RNN.

2016-04-22
  • Speed up process of the test performance about theano version of Generationg the similar questions' vectors based on RNN.
  • Help conduct the presentation of text group(Tang poetry and Songci generation and Intelligent QA system) for Tsinghua University's 105 anniversary.

2016-04-23
  • Use entity match rule 1(dpk, ix, lpk sv + machine number) to improve the accuracy from 38% to 58%.

2016-04-24
  • Use entity match rules 2+3(dps, s, e, fp, tps, q, 200 + machine number and machine number only) to improve the accuracy from 58% to 72.09%.
  • Use entity match rules 4(dpk, ix, lpk sv, dps, s, e, fp, tps, q + machine number + English chacracters) to improve the accuracy from 72.09% to 72.86%.
  • Use entity match rules 5(machine number + English chacracters) to improve the accuracy from 72.86% to 76.48%.
  • Use simlar pair match rules 1(“快”和“速度”、“复写”和“拷贝”等等) to improve the accuracy from 76.48% to 82.39%.

Plan to do next week

  • To implement tensorflow version of RNN/LSTM Max margin vector training.
  • To implement attention chatting model with xiaobing corpus.

Interested papers

  • Cascading Bandits: Learning to Rank in the Cascade Model(ICML 2015) [pdf]