Why I gave your paper a Strong Reject [Repost]
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2017-08-04
华南师范大学
计算机学院

原文链接:http://matt-welsh.blogspot.com/2016/04/why-i-gave-your-paper-strong-reject.html


Why I gave your paper a Strong Reject


I'm almost done reviewing papers for another conference, so you know what that means -- time to blog.


I am starting to realize that trying to educate individual authors through my witty and often scathing paper reviews may not be scaling as well as I would like. I wish someone would teach a class on "How to Write a Decent Goddamned Scientific Paper", and assign this post as required reading. But alas, I'll have to make do with those poor souls who stumble across this blog. Maybe I'll start linking this post to my reviews.


All of this has probably been said before (strong reject) and possibly by me (weak accept?), but I thought I'd share some of the top reasons why I tend to shred papers that I'm reviewing.


(Obligatory disclaimer: This post represents my opinion, not that of my employer. Or anyone else for that matter.)


The abstract and intro suck. By the time I'm done reading the first page of the paper, I've more or less decided if I'm going to be reading the rest in a positive or negative light. In some cases, I won't really read the rest of the paper if I've already decided it's getting The Big SR. Keep in mind I've got a pile of 20 or 30 other papers to review, and I'm not going to spend my time picking apart the nuances of your proofs and evaluation if you've bombed the intro.


Lots of things can go wrong here. Obvious ones are pervasive typos and grammatical mistakes. (In some cases, this is tolerable, if it's clear the authors are not native English speakers, but if the writing quality is really poor I'll argue against accepting the paper even if the technical content is mostly fine.) A less obvious one is not clearly summarizing your approach and your results in the abstract and intro. Don't make me read deep into the paper to understand what the hell you're doing and what the results were. It's not a Dan Brown novel -- there's no big surprise at the end.


The best papers have really eloquent intros. When I used to write papers, I would spend far more time on the first two pages than anything else, since that's what really counts. The rest of the paper is just backing up what you said there.


Diving into your solution before defining the problem. This is a huge pet peeve of mine. Many papers go straight into the details of the proposed solution or system design before nailing down what you're trying to accomplish. At the very least you need to spell out the goals and constraints. Better yet, provide a realistic, concrete application and describe it in detail. And tell me why previous solutions don't work. In short -- motivate the work.


Focusing the paper on the mundane implementation details, rather than the ideas. Many systems papers make this mistake. They waste four or five pages telling you all about the really boring aspects of how the system was implemented -- elaborate diagrams with boxes and arrows, detailed descriptions of the APIs, what version of Python was used, how much RAM was on the machine under the grad student's desk.


To first approximation, I don't care. What I do care about are your ideas, and how those ideas will translate beyond your specific implementation. Many systems people confuse the artifact with the idea -- something I have blogged about before. There are papers where the meat is in the implementation details -- such as how some very difficult technical problem was overcome through a new approach. But the vast majority of papers, implementation doesn't matter that much, nor should it. Don't pad your paper with this crap just to make it sound more technical. I know it's an easy few pages to write, but it doesn't usually add that much value.


Writing a bunch of wordy bullshit that doesn't mean anything. Trust me, you're not going to wow and amaze the program committee by talking about dynamic, scalable, context-aware, Pareto-optimal middleware for cloud hosting of sensing-intensive distributed vehicular applications. If your writing sounds like theautomatically-generated, fake Rooter paper ("A theoretical grand challenge in theory is the important unification of virtual machines and real-time theory. To what extent can web browsers be constructed to achieve this purpose?"), you might want to rethink your approach. Be concise and concrete. Explain what you're doing in clear terms. Bad ideas won't get accepted just because they sound fancy.


Overcomplicating the problem so you get a chance to showcase some elaborate technical approach. A great deal of CS research starts with a solution and tries to work backwards to the problem. (I'm as guilty of this, too.) Usually when sitting down to write the paper, the authors realize that the technical methods they are enamored with require a contrived, artificial problem to make the methods sound compelling. Reviewers generally aren't going to be fooled by this. If by simplifying the problem just a little bit, you render your beautiful design unnecessary, it might be time to work on a different problem.


Figures with no descriptive captions. This is a minor one but drives me insane every time. You know what I mean: A figure with multiple axes, lots of data, and the caption says "Figure 3." The reviewer then has to read deep into the text to understand what the figure is showing and what the take-away is. Ideally, figures should be self-contained: the caption should summarize both the content of the figure and the meaning of the data presented. Here is an example from one of my old papers:



Isn't that beautiful? Even someone skimming the paper -- an approach I do notendorse when it comes to my publications -- can understand what message the figure is trying to convey.


Cursory and naive treatment of related work. The related work section is not a shout-out track on a rap album ("This one goes out to my main man, the one and only Docta Patterson up in Bezerkeley, what up G!"). It's not there to be a list of citations just to prove you're aware of those papers. You're supposed to discuss the related work and place it in context, and contrast your approach. It's not enough to say "References [1-36] also have worked on this problem." Treat the related work with respect. If you think it's wrong, say so, and say why. If you are building on other people's good ideas, give them due credit. As my PhD advisor used to tell me, stand on the shoulders of giants, not their toes.




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