In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Q: Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. 
 Question: what levels of document preprocessing are looked at?

A: raw text text cleaning through document logical structure detection removal of keyphrase sparse sections of the document
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Q: The contributions of this work are: 
 Question: Do they look for inconsistencies between different UD treebanks?

A: Yes
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Q: Among all available parameters to tune the D2V algorithm released by Gensim, six of them were selected for optimisation BIBREF14. The window_size parameter affects the size of the sliding window used to parse texts. The alpha parameter represents the learning rate of the network. The sample setting allows the model to reduce the importance given to high-frequency words. The dm parameter defines the training used architecture (PV-DM or PV-DBOW).  
 Question: What Doc2Vec architectures other than PV-DBOW have been tried?

A:
PV-DM
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