1. Rebuttal的基本格式
一般rebuttal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的关注问题，做到释义清楚且废话少说。目前我的rebuttal的格式一般如下所示：
<img src="https://www.lw881.com/uploadfile/202212/dd4693ac2215c11.png" data-rawwidth="1592" data-rawheight="1124" class="origin_image zh-lightbox-thumb" width="1592" data-original="https://pic1.zhimg.com/v2-d3e09fcfcd5ca745557fa83ad26bf580_r.png">
其中，不同reviewer提出的同样的问题可以不用重复回答，可以直接"Please refer to A2 to reviewer#1"。结构清晰的rebuttal能够对reviewer和area chair提供极大的便利，也便于理解。
2. Rebuttal的内容
Rebuttal一定要着重关注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。根据我的审稿经验，那些明显在回避一些问题的response只会印证自己的负面想法；而能够直面reviewer问题，有理有据指出reviewer理解上的偏差的response则会起到正面的效果。（PS: 如果自己的工作确实存在reviewer提出的一些问题，不妨表示一下赞同，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，有最后一丝机会，当然，最基本的礼貌还是要有，不过很有可能有负面的效果，参考今年ICLR LipNet论文rebuttal 。
3. Rebuttal的意义
大家都知道通过rebuttal使reviewer改分的概率很低，但我认为rebuttal是一个尽人事的过程，身边也确实有一些从reject或borderline通过rebuttal最终被录用的例子。尤其像AAAI/IJCAI这种AI大领域的会议，最近两年投稿动则三四千篇，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!
----------------------------------------------------------------------------------
最后贴一下LeCun在CVPR2012发给pc的一封withdrawal rebuttal镇楼（该rebuttal被pc做了匿名处理），据说促成了ICLR的诞生，希望自己以后也有写这种rebuttal的底气：）
Hi Serge,
We decided to withdraw our paper #[ID no.] from CVPR "[Paper Title]" by [Author Name] et al.
We posted it on ArXiv: [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]
针对每一个问题列出详细清单。首先楼主需要驳斥或解释审稿人提出的问题有可能是审稿人的意见比较负面，而且要有理有据，针对审稿人提出的一些合理的问题你可以适当修改原文，有的放矢，编辑觉得楼主的文章有可取之处，逐一击破，这种情况下需要写rebuttal letter，但要做具体说明
（非原创——————来自‘曼联dds’）
另外可以参考，这里说的很具体
