Course talk:CPSC522/Analysis of hierarchical prior for Language modeling

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Thread titleRepliesLast modified
Critique116:14, 19 April 2018
Helpful suggestions116:13, 19 April 2018

On a scale of 1 to 5, where 1 means "strongly disagree" and 5 means "strongly agree" please rate and comment on the following:

  • The topic is relevant for the course. 5
  • The writing is clear and the English is good. 5
  • The page is written at an appropriate level for CPSC 522 students (where the students have diverse backgrounds). 4
  • The formalism (definitions, mathematics) was well chosen to make the page easier to understand. 5
  • The abstract is a concise and clear summary. 0
  • There were appropriate (original) examples that helped make the topic clear. 4
  • There was appropriate use of (pseudo-) code. 0
  • It had a good coverage of representations, semantics, inference and learning (as appropriate for the topic). 5
  • It is correct. 5
  • It was neither too short nor too long for the topic. 5
  • It was an appropriate unit for a page (it shouldn't be split into different topics or merged with another page). 5
  • It links to appropriate other pages in the wiki. 3
  • The references and links to external pages are well chosen. 4
  • I would recommend this page to someone who wanted to find out about the topic. 5
  • This page should be highlighted as an exemplary page for others to emulate. 5

If I was grading it out of 20, I would give it: 19

  • A good page; no real complaints other than a missing abstract.
  • Would it be possible to include some code?
  • Maybe you can include links to the Recurrent Neural Network page, as well as links to LSTM and GRU.
  • Nitpick: You can format the evaluation results' images so that they run horizontal across the page.
02:13, 19 April 2018

Thanks May :)

Will include an abstract and code snippets. Cheers

16:14, 19 April 2018
 

Helpful suggestions

Hi Kevin,

Its a thorough document. Here are three suggestions:

  # Can you include a link to your code repository &/or a code snippet?  
  # Can you include an abstract?
  # Can you describe your experimental setup - i.e I learnt only in your conclusion that you used TF and 2 models. Maybe you can put that before and also mention what the models are.

Regards, Surbhi

05:50, 19 April 2018

Thank you for your feedback Surbhi :) Will make the changes you suggested.

16:13, 19 April 2018