I always thought that one of the defining characteristics of AI conferences is the significant amount of inter-reviewer discussions on each paper.
In planning, for example, it is not all that unheard of to have the discussions that are as long as the paper (yes we are thinking of you, **@trik!).
Having also handled many AI&Web papers over years, I did have a hunch that the amount of discussion is not same across areas.
Now that we have access to the reviews for the whole 2300 papers of IJCAI, we decided to see how the various areas stack up.
We counted the number of words in all the discussion comments for each paper, and then averaged them across each area. Here is what we got:
So, papers in Planning & Scheduling, Heuristic Search, KR, Constraints, and MAS areas get significantly more discussion, compared to Machine Learning, NLP and AI&Web.
The AIW statistic is somewhat understandable as the reviewers there are not just from AI community and may have different cultural norms.
The Machine Learning statistic, however, is worrisome especially since a majority of submissions are in ML. Some of the ML colleagues I talked to say that things are not that different at other ML conferences (ICML, NIPS etc). Which makes me wonder, whether the much talked about NIPS experiment is a reflection of peer reviewing in general, or peer reviewing in ML...
In case you are wondering, here is the plot for the length of reviews (again measured in terms of the number of words across all reviews). Interestingly, AIW submissions have longer reviews than ML and NLP!
So you know!
Rao
(with all real legwork from Lydia, aka IJCAI-16 data scientist...)
In planning, for example, it is not all that unheard of to have the discussions that are as long as the paper (yes we are thinking of you, **@trik!).
Having also handled many AI&Web papers over years, I did have a hunch that the amount of discussion is not same across areas.
Now that we have access to the reviews for the whole 2300 papers of IJCAI, we decided to see how the various areas stack up.
We counted the number of words in all the discussion comments for each paper, and then averaged them across each area. Here is what we got:
So, papers in Planning & Scheduling, Heuristic Search, KR, Constraints, and MAS areas get significantly more discussion, compared to Machine Learning, NLP and AI&Web.
The AIW statistic is somewhat understandable as the reviewers there are not just from AI community and may have different cultural norms.
The Machine Learning statistic, however, is worrisome especially since a majority of submissions are in ML. Some of the ML colleagues I talked to say that things are not that different at other ML conferences (ICML, NIPS etc). Which makes me wonder, whether the much talked about NIPS experiment is a reflection of peer reviewing in general, or peer reviewing in ML...
In case you are wondering, here is the plot for the length of reviews (again measured in terms of the number of words across all reviews). Interestingly, AIW submissions have longer reviews than ML and NLP!
So you know!
Rao
(with all real legwork from Lydia, aka IJCAI-16 data scientist...)