Body: <p>I am playing with a large dataset of hotel reviews, which contains both positive and negative reviews (the reviews are labeled). I want to use this dataset to perform textual style transfer - given a positive review, output a negative review which address the same thing. For example, if the positive review mentioned how spacious the rooms are, I want the output to be a review that complains about the small and claustrophobic rooms.</p>
  
  <p>However, I don't have positive review-negative review pairs for the training. I was thinking that maybe I could create those pairs myself, but I'm not sure what is the best way to do that. Simple heuristics like jaccard index and such didn't give the desired results. </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-30T19:43:52.673Z
Comments.Text: This seems like a tricky data set to create automatically. Do you have a different goal you want to eventually achieve, once you have this data set? Put differently, why are you building such a data set? By the way, "style transfer" can very often be equated with "translation" in NLP.
Comments.UserDisplayName: Mathias Müller
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-02-03T07:13:42.710Z
Comments.Text: Well, my goal is to show that supervised style transfer can work in practice in complex cases, so I could get funds for labeling a very challenging dataset :)
Comments.UserDisplayName: Nadav Borenstein
Tags: <natural-language-processing><text-generation>
Title: How to tell if two hotel reviews addressing the same thing
UserDisplayName: Nadav Borenstein
ThreadText: # How to tell if two hotel reviews addressing the same thing
  
  (Asked by: username_0 on 2020-01-30)
  
  I am playing with a large dataset of hotel reviews, which contains both positive and negative reviews (the reviews are labeled). I want to use this dataset to perform textual style transfer - given a positive review, output a negative review which address the same thing. For example, if the positive review mentioned how spacious the rooms are, I want the output to be a review that complains about the small and claustrophobic rooms.
  
  However, I don't have positive review-negative review pairs for the training. I was thinking that maybe I could create those pairs myself, but I'm not sure what is the best way to do that. Simple heuristics like jaccard index and such didn't give the desired results.
  
  **Tags:** natural-language-processing, text-generation
  > **Comments:**
  > **username_1**: This seems like a tricky data set to create automatically. Do you have a different goal you want to eventually achieve, once you have this data set? Put differently, why are you building such a data set? By the way, "style transfer" can very often be equated with "translation" in NLP.  
  > **username_0**: Well, my goal is to show that supervised style transfer can work in practice in complex cases, so I could get funds for labeling a very challenging dataset :)  
  
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  ## 0 answers
  
  