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Have not found Monte Carlo Sampling in the code #21

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fangkuann opened this issue May 28, 2019 · 3 comments
Open

Have not found Monte Carlo Sampling in the code #21

fangkuann opened this issue May 28, 2019 · 3 comments

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@fangkuann
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fangkuann commented May 28, 2019

Hi,
Thanks for releasing the code for active-qa.
After browsing the code, I did not find Monte-Carlo Sampling in the training stage. It seems that each training instance consists of only one 「query, reformulated_query, reward」 tuple. Therefore, the reward is the same for each token in one reformulated query.
I don't know whether the suspicion is right. If it is right, what will model perform with or without Monte-Carlo sampling? Maybe using only one instance for Monte Carlo sampling is like the relation between stochastic gradient descent and gradient descent?
Thank you

@godfly
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godfly commented Sep 24, 2020

me the same, have you finger out the problem?or get a new version code? @fangkuann

@fangkuann
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me the same, have you finger out the problem?or get a new version code? @fangkuann

I didn't try that. But we apply the paper's training method in our query rewrite module, then the retrieve performance could be enhanced. We further improved this method by adding a value network, for details you may refer to this Chinese technic article https://www.6aiq.com/article/1577969687897.

@godfly
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godfly commented Oct 9, 2020

me the same, have you finger out the problem?or get a new version code? @fangkuann

I didn't try that. But we apply the paper's training method in our query rewrite module, then the retrieve performance could be enhanced. We further improved this method by adding a value network, for details you may refer to this Chinese technic article https://www.6aiq.com/article/1577969687897.

@fangkuann Yes, I follow this article to here. May I ask some question by email? I couldn't found a way to concat you. Send a message to yanggodfly1994@gmail.com if it's ok, thanks a lot

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