I'll be honest: I've had my feelings hurt by scathing reviews more than a few times. In grad school I remember even crying over a review that I thought was particular pernicious. My skin has thickened a bit over time, though often in the not-so-helpful manner of dismissing reviews that I don't like as "they didn't get it," which defeats one of the two primary purposes of reviews in the first place (providing feedback; the other: making accept/reject decisions).
The thing that's hard to reconcile is that I really like most of the people in our community, and everyone I meet at least seems really friendly.
When doing mock reviews with grad students, I'll often tell them to keep in mind that there's a good chance that the author is, or later will be, a friend of theirs. It's possible to provide feedback to a friend in such a way that you don't hurt their feelings.
I've recently started doing something else (in addition to the above suggestion). I don't use the words "you" or "the authors" or even "I." The review of a scientific contribution is not about me and it's not about the authors. It's about the method, the experiments and the contribution. I see little reason why you need to mention anything related to the people involved. (One exception: "I" is often useful in hedging, like the previous sentence, which would be more forceful if I just said "There is little reason...") Perhaps we could even integrate this into START...
This is, of course, similar to the pop-psych advice of talking to loved ones about "actions" rather than "the person." For instance: "I hate you for spilling coffee and not cleaning it up" versus "I hate having coffee spilt on the floor." Or something. I'm sure others can come up with better examples.
My current approach is to write my review with this in mind, and then go back and search for all occurrences of my outlawed nouns, and rewrite these sentences. Often in the process of doing this, I become aware that in many of the cases what I've said really does sound like an attack, and with the very small edit this effect is removed or at least greatly reduced.
I realize I've now just given a pretty good signal for people reading reviews to see if they were written by me or not. Here's a solution: everyone should adopt this policy and then my reviews will no longer be so obvious.
But overall, I really think we should be nice to each other. Perhaps fewer people will depart from the field if they're not constantly battered down by harsh reviews, and then we'll all be better off.
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.)
26 April 2014
14 April 2014
Waaaah! EMNLP six months late :)
Okay, so I've had this file called emnlp.txt sitting in my home directory since Oct 24 (last modification), and since I want to delete it, I figured I'd post it here first. I know this is super belated, but oh well, if anyone actually reads this blog any more, you're the first to know how I felt 6 months ago. I wonder if I would make the same calls today... :)
Happy Spring from DC!
- A Log-Linear Model for Unsupervised Text Normalization (Yi Yang and Jacob Eisenstein)
- Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning [TACL] (Minh-Thang Luong, Michael C. Frank, Mark Johnson)
- Document Summarization via Guided Sentence Compression (Chen Li, Fei Liu, Fuliang Weng and Yang Liu)
- Sarcasm as Contrast between a Positive Sentiment and Negative Situation (Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert and Ruihong Huang)
- Identifying Phrasal Verbs Using Many Bilingual Corpora (Karl Pichotta and John DeNero)
- Violation-Fixing Perceptron and Forced Decoding for Scalable MT Training (Heng Yu, Liang Huang and Haitao Mi)
- Where Not to Eat? Predicting Restaurant Inspections from Online Reviews (Jun Seok Kang, Polina Kuznetsova, Michael Luca and Yejin Choi)
- Inducing Document Plans for Concept-to-text Generation (Ioannis Konstas and Mirella Lapata)
- Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading [TACL] (Sumit Basu, Charles Jacobs and Lucy Vanderwende)
Happy Spring from DC!