Michael michaelsaxon

SSH keypair setup for GitHub (or GitHub/GitLab/BitBucket, etc, etc)

Create a repo.

Make sure there is at least one file in it (even just the README.md)

Generate a SSH key pair (private/public):

ssh-keygen -t rsa -C "[email protected]"

The Impossible Task of Conference SACs/PCs or How I lost 3 Nights of Sleep

I am writing this post in order to share my thoughts on the processes behind acceptance/rejection decisions in top-tier (NLP) conferences. I'll first discuss the process and then share some thoughts on its shortcomings.

Before we start, a bit about me. I am an assistant professor (aka, rather junior: I have been in this position for less than 4 years, following my PhD studies and a short postdoc) working on NLP, with a focus on multilingualism and low-resource settings. While I have submitted, published at, and reviewed for *ACL conferences and workshops for many years, it was at EMNLP'23 that I was a Senior Area Chair (SAC) for the first time.

The Conference Paper Pipeline

Let's first briefly outline the process that a paper undergoes, from submission to decision:

ACL is not an AI Conference (?)

Yoav Goldberg, August 2024

In her "Presidential Address" at the ACL 2024, Emily Bender gave a talk called "ACL is not an AI Conference". For those who did not attend (or were not paying close attention), you can find the slides in the following link: https://faculty.washington.edu/ebender/papers/ACL_2024_Presidential_Address.pdf

Somewhat surprisingly, I found myself agreeing with some core aspects of her argument. Perhaps less surprisingly, there is also a substantial part which I strongly disagree with. This text is a response to this address, and, beyond just responding, may also shed some light on what is ACL, and what is NLP. I of course welcome discussion on these topics, either on the comments section here (unfortunately not very convenient) or on Twitter (not convenient in a different way). Ok, Let's go.

ACL is not a Computational Linguistics Conference

🚨 (Update June 2025) 🚨 This little post has gained a huge amount of interest, for which I am very grateful. However, it's been overwhelming to keep up with 40-50 reachouts per week. To make things manageable, I am selectively focusing on referrals for full-time Applied Scientist and Data Scientist roles only. The rest of this post still contains tips for other tech roles (Software Engineer, Data Engineer, BIE, etc) and details on the hiring process in general; I hope these are valuable for you. Good luck on your journey!

I am always happy to provide referrals for folks applying to Amazon for a variety of roles. Amazon is a FAANG, so I know that landing a job there can make a big difference to someone's career, and I am happy to spend the time to provide a referral.

If you are interested, please read the points below very carefully before reaching out (I only accept requests via LinkedIn). This is to save time on both sides 😄 I usually check around

	library(tidyverse)



	# Prepare data with Praat and Matt Winn's functions
	# https://github.com/ListenLab/Praat/blob/master/praat_spectrogram_functions.R

	# file <- "my-file.Spectrogram"
	#
	# df_spectrogram <- file %>%