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yoavg / ACL.js
Created September 9, 2015 08:07
Updated the zotero ACL translator to include titles as well as author names in selection list.
"translatorID": "f4a5876a-3e53-40e2-9032-d99a30d7a6fc",
"label": "ACL",
"creator": "Nathan Schneider, Yoav Goldberg",
"target": "^https?://(www[.])?aclweb\\.org/anthology/[^#]+",
"minVersion": "1.0.8",
"maxVersion": "",
"priority": 100,
"inRepository": true,
"translatorType": 4,
import sys
import numpy as np
import random
sys.argv += ["--dynet-mem", "1000", "--dynet-seed", "10", "--dynet-gpu-ids" , "1" ]
from dynet import *
import sys
import numpy as np
import random
sys.argv += ["--dynet-mem", "1000", "--dynet-seed", "10", "--dynet-gpu-ids" , "1" ]
from dynet import *
"cells": [
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4gram language models share secrets too...\n",
"_Yoav Goldberg, 28 Feb, 2018._\n",
"In [a recent research paper]( titled \"The Secret Sharer:\n",

On virtual spaces (for scientific conferences)

The title is a bit broad. What I am going to write about is I complained quite a bit re how recent conferences used the gather platform. Here I try to be more constructive, and explain why I think things were bad, and also how I think they can be improved (substantially).

I think is a fantastic interface, and I think it was mis-used or mal-used in some recent xACL conferences (EACL 2021, EMNLP 2020). It is really disappointing, as there is so much potential, which was not only left unfulfilled, but even in some cases was worse than not having gather at all. This post will try to explain what I think was bad, and how I think things can be improved.

yoavg / ngram-lm-leak.ipynb
Created February 27, 2018 22:51
Simple 4gram-lm also "leak secrets"
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Thoughts and some criticism on "Re-imagining Algorithmic Fairness in India and Beyond".

Yoav Goldberg, Jan 30, 2021

This new paper from Google Research Ethics Team (by Sambasivan, Arnesen, Hutchinson, Doshi, and Prabhakaran) touches on a very imortant topic: research (and supposedly also applied) work on algorithmic fairness---and more broadly AI-ethics---is US-centric[*], reflecting US subgroups, values, and methods. But AI is also applied elsewhere (for example, India). Do the methods and result developed for/in the US transfer? The answer is, of course, no, and the paper is doing a good job of showing it. If you are the kind of person who is impressed by the number of citations, this one has 220, a much higher number than another paper (not) from Google Research that became popular recently and which boasts many citations. I think this current paper (let's call it "the India Paper") is substantially more important, given that it raises a very serious issue that

Putting papers on arxiv early vs the protections of blind review

The tension between putting papers on arxiv as soon as possible and the double-blind peer review process is ever present. Some people favor the fast-pace of progress facilitated by making papers available before or during the peer review process, while others favor the protection of double-blind reviewing (actually, of author-blind reviewing. reviewer-anonymity is not part of the debate).

As I now serve on an ACL committee which is tasked at assessing this tension, I've spend a longer-then-usual time thinking about it, and came up with an analysis which I find informative, and which others may also find useful. These are my personal opinions, and are not representative of the committee. Though naturally, I will share them there as well.

The analysis examines the dynamics of review bias due to author identities being made exposed through a pre-print, and its effect on other authors at the same conference. The conclusion, as usual with me,

yoavg /
Last active November 9, 2023 04:32
A criticism of Stochastic Parrots

A criticism of "On the Dangers of Stochastic Parrots: Can Languae Models be Too Big"

Yoav Goldberg, Jan 23, 2021.

The FAccT paper "On the Dangers of Stochastic Parrots: Can Languae Models be Too Big" by Bender, Gebru, McMillan-Major and Shmitchell has been the center of a controversary recently. The final version is now out, and, owing a lot to this controversary, would undoubtly become very widely read. I read an earlier draft of the paper, and I think that the new and updated final version is much improved in many ways: kudos for the authors for this upgrade. I also agree with and endorse most of the content. This is important stuff, you should read it.

However, I do find some aspects of the paper (and the resulting discourse around it and around technology) to be problematic. These weren't clear to me when initially reading the first draft several months ago, but they became very clear to me now. These points are for the most part