Is anyone else on the West Coast already up and surprised to see an email from #WeCNLP2020 saying the event starts in "59 minutes" when the schedule says 8am start?
Seems like an auto-generated system from the online platform because the site is opening at 7, though the program doesn't start until 8.
Given that this one is WEST COAST NLP and for once is actually in our timezone, it would be nice to not be harassed by emails making us feel late...
Seems like just a little bit of poor design by the folks at @hopinofficial if those notifications are auto-generated. At the very least it could say something like
"Your venue is opening" rather than "X is starting".

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More from @emilymbender

30 Oct
I was feeling a little ranty this morning, but there's actually also some interesting points about context and pragmatics here, for when we write (or cause machines to write) text that will be interpreted in contexts we aren't directly participating in:
Surely, from the platform's point of view, WeCNLP is starting at 7am. For them, WeCNLP refers to an event with "start" and "end" times they have to program into their platform, so that people who have registered can access the platform during those times. >>
But for people *attending* WeCNLP (the addressees of that email), WeCNLP refers to an event with a specific internal schedule, of talks and informal meeting times. >>
Read 7 tweets
14 Sep
This is a really important paper for #NLProc, #ethNLP and #ethicalAI folks.

1/n
The authors look deep into a use case for text that is ungrounded in either the world or any commitment what's being communicated but nonetheless fluent, apparently coherent, and of a specified style. You know, exactly #GPT3's specialty.

2/n
What's that use case? The kind of text needed, and apparently needed in quantity, for discussion boards whose purpose is recruitment and entrenchment in extremist ideologies.

3/n
Read 10 tweets
21 Jun
@robiupui I totally get that this is the most difficult option in many ways, but also I might be able to help because I have been teaching in this format since 2007, in order to accommodate both distance & local students in the MS program that I run. >>
@robiupui The problems to solve are:

1 Instructor audible to remote & local students
2 Slides visible to remote & local students
3 Instructor gestures visible to remote & local students
4 All students can ask questions/answer questions/comment
5 All students can hear student contributions
@robiupui 6 Remote students can turn in homework/take exams
7 Remote students can connect with out of class help (office hours, bulletin boards)
8 Remote students can collaborate with others (local or not).

6-8 are easy though and I imagine not what you're asking about. >>
Read 12 tweets
17 Jun
For slightly tedious reasons, I've ended up with the homework assignment of finding scholarly literature analyzing how so-called IQ tests are biased. I've one some digging and that literature is a *mess*, so I'm reaching out here in case anyone has appropriate cites to hand. >>
From the fact that IQ tests are one tool used to push Black children in the US into special ed classrooms [1] and the fact that the whole history of IQ testing is bound up with eugenics [2] >>

[1] kalw.org/post/legacy-mi…
[2] nature.com/articles/d4158…
... the obvious conclusion is that population-level differences in IQ scores are due in large part to the IQ test itself either being a situation that is more familiar/normative for white kids or involving questions that draw on white culture (while pretending to be neutral). >>
Read 5 tweets
13 Dec 19
Tonight in briefly got tangled in a rather pointless Twitter argument about #AIethics—pointless because the other party isn’t interested in discussing in good faith. One point of clarity has come out of that though, in the responses of a few of us and I want to pull it out here:
Ethical considerations are not a separate concern from whether the AI research is “sound”: sound AI research requires not only valid math but also sensible task design.
A lot of the ethically questionable things we’re seeing (predicting faces from voices, predicting “intellectual ability” from short text responses, etc) are cases where it doesn’t make sense to predict A from B, by any model.
Read 9 tweets
23 Jul 19
Niven & Kao's upcoming #acl2019nlp paper "Probing Neural Network Comprehension of Natural Language Arguments" asks exactly the right question of unreasonable performance: "what has BERT learned about argument comprehension?"

Preprint:
arxiv.org/abs/1907.07355

/1
They show, with careful experiments, that in fact, “BERT has learned nothing about argument comprehension.” Rather: “As our learners get stronger, controlling for spurious statistics becomes more important in order to have confidence in their apparent performance.” /2
This kind of careful work, featuring careful attention to the data, is exactly what #NLProc needs more of! /3
Read 4 tweets

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