Tianyi Luo 2016-04-25

Plan to do this week

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

Work done in this week

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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.

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2016-04-19

• Optimize theano version of Generationg the similar questions' vectors based on RNN.

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2016-04-20

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

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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.

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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.

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2016-04-23

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

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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]