要明确Rebuttal的目的，首先是为了论文能够被接受。要坚持团结可能accept的，讨好可能reject的，这个基本原则不能变。所以，写 Rebuttal 的主题应该是：澄清和说服。对于评审来说，需要澄清疑问，回答问题，纠正误解，真诚的吸收反馈，改进你的工作。
然后，这一阶段可以帮助论文作者了解自己的工作还有哪里可做提高，同时要能够驳斥审稿人对论文内容的误解。这一阶段的受众是审稿人和领域主席。一份好的Rebuttal将有助于审稿人之间的讨论和领域主席做出决定。
01
Rebuttal的基本格式
Rebuttal 一般都有比较严格的篇幅要求，比如不能多于500或600个词。所以 Rebuttal 的关键是要在有限的篇幅内尽可能清晰全面的回应数个reviewer的问题，做到释义清楚且废话少说。
因此在写的时候，真真要做到惜字如金了——
· 能用简写的尽量用简写（例如experiment--exp.）
· 减少标点之间的空格
· 少用标点，不复杂的从句可以多一点
· 如果实在篇幅不够，只针对那些重要问题回答
建议Rebuttal部分可以参考下面的格式：
首先，开头一句话感谢并肯定审稿人的工作。如：
Thanks for your careful and valuable comments，we will explain your concerns point by point。
然后针对每个reviewer分别回复。而在回复每个reviewer时，也按照每个问题分别回复。先简要复述问题，然后回答。
Reviewer#1：
Q1:..............
A1:.............
Q2:............
A2:...........
Reviewer#2：
Q1:..............
A1:.............
Q2:............
A2:...........
如果有不同reviewer提出同样的问题，可以不用重复回答，直接用"Please refer to A2 to reviewer#1"解决即可。结构清晰的rebuttal能够给Reviewer 和 Area Chair 提供极大的便利。
02
Rebuttal的内容
写Rebuttal时一定要关注 reviewer 提出的重点问题，这些才是决定reviewer 的态度的关键，不要尝试去回避这种问题。回答这些问题的时候要直接且不卑不亢，保持尊敬的同时也要敢于指出 reviewer 理解上的问题。那些明显在回避一些问题的 response 只会加强审稿人的负面评价；而能够直面 reviewer 的问题，有理有据指出 reviewer 理解上的偏差的 response 才会起到正面的效果。
如果自己的工作确实存在 reviewer 提出的一些问题，不妨对reviewer的意见表示赞同，同时强调自己论文的亮点和 contribution，并把针对这个问题的改进列为future work。
面对由于 reviewer 理解偏差造成全部 reject 的情况，言辞激烈一点才有可能引起 Area Chair 的注意，博得最后一丝机会，当然，要注意言辞激烈的同时最基本的礼貌还是要有。
03
Rebuttal的意义
大家都知道通过 rebuttal 使 reviewer 改分的概率很低，rebuttal是一个尽人事的过程，但也确实有一些通过 rebuttal 从 reject 或 borderline 最终被录用的例子。尤其像 AAAI / IJCAI 这种 AI 大领域的会议，投稿论文数量巨大，这么多 reviewer 恰好是自己小领域同行的概率很低，难免会对工作造成一些理解上的偏差甚至错误，此时的 rebuttal 就显得特别重要。所以对于处于 borderline 或者由于错误理解造成低分的论文，一定!一定!一定!要写好rebuttal!
最后让我们一起来欣赏 LeCun 在 CVPR 2012 发给 PC 的 一封withdrawal rebuttal（该rebuttal被pc做了匿名处理），据说促成了ICLR的诞生——
Hi Serge,
We decided to withdraw our paper #[ID no.] from CVPR "[Paper Title]" by [Author Name] et al.
We posted it on ArXiv: http://arxiv.org/ [Paper ID] .
We are withdrawing it for three reasons: 1) the scores are so low, and the reviews so ridiculous, that I don't know how to begin writing a rebuttal without insulting the reviewers; 2) we prefer to submit the paper to ICML where it might be better received; 3) with all the fuss I made, leaving the paper in would have looked like I might have tried to bully the program committee into giving it special treatment.
Getting papers about feature learning accepted at vision conference has always been a struggle, and I've had more than my share of bad reviews over the years. Thankfully, quite a few of my papers were rescued by area chairs.
This time though, the reviewers were particularly clueless, or negatively biased, or both. I was very sure that this paper was going to get good reviews because: 1) it has two simple and generally applicable ideas for segmentation ("purity tree" and "optimal cover"); 2) it uses no hand-crafted features (it's all learned all the way through. Incredibly, this was seen as a negative point by the reviewers!); 3) it beats all published results on 3 standard datasets for scene parsing; 4) it's an order of magnitude faster than the competing methods.
If that is not enough to get good reviews, I just don't know what is.
So, I'm giving up on submitting to computer vision conferences altogether. CV reviewers are just too likely to be clueless or hostile towards our brand of methods. Submitting our papers is just a waste of everyone's time (and incredibly demoralizing to my lab members)
I might come back in a few years, if at least two things change:
- Enough people in CV become interested in feature learning that the probability of getting a non-clueless and non-hostile reviewer is more than 50% (hopefully [Computer Vision Researcher]'s tutorial on the topic at CVPR will have some positive effect).
- CV conference proceedings become open access.
We intent to resubmit the paper to ICML, where we hope that it will fall in the hands of more informed and less negatively biased reviewers (not that ML reviewers are generally more informed or less biased, but they are just more informed about our kind of stuff). Regardless, I actually have a keynote talk at [Machine Learning Conference], where I'll be talking about the results in this paper.
Be assured that I am not blaming any of this on you as the CVPR program chair. I know you are doing your best within the traditional framework of CVPR.
I may also submit again to CV conferences if the reviewing process is fundamentally reformed so that papers are published before they get reviewed.
You are welcome to forward this message to whoever you want.
I hope to see you at NIPS or ICML.
Cheers,
-- [Author]
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