It is now 2023, and virtually every journal I review for has a broken website, which further penalizes me for volunteer work I ought to be paid for. This is really unacceptable. Maybe some of the big publishers can take a tiny bite out of their massive revenues (Springer Nature apparently pulled down 1.72b USD in revenue in 2021) and invest it into actually testing their the CRUD apps.
Asides
Large LMs and disinformation
I have never understood the idea that large LMs are uniquely positioned to enable the propagation of disinformation. Let us stipulate, for sake of argument, that large LMs can generate high-quality disinformation and that its artificial quality (i.e., not generated by human writers) cannot be reliably detected either by human readers nor by computational means. At the same time, I know of no reason to suppose that large LMs can generate better (less detectable, more plausible) disinformation than can human writers. Then, it is hard to see what advantage there is to using large LMs for disinformation generation beyond a possible economic benefit realized by firing PR writers and replacing them with “prompt engineers”. Ignoring the dubious economics—copywriters are cheap, engineers are expensive—there is a presupposition that disinformation needs to scale, i.e., be generated in bulk, but I see no reason to suppose this either. Disinformation, it seems to me, comes to us either in the form of “big lies” from sources deemed reputable by journalists and lay audiences (think WMDs), or increasingly, from the crowds (think Qanon).
e- and i-France
It will probably not surprise the reader to see me claim that France and French are both sociopolitical abstractions. France is, like all states, an abstraction, and it is hard to point to physical manifestations of France the state. But we understand that states are a bundle of related institutions with (mostly) shared goals. These institutions give rise to our impression of the Fifth Republic, though at other times in history conflict between these institutions gave rise to revolution. But currently the defining institutions share a sufficient alignment that we can usefully talk as if they are one. This is not so different from the i-language perspective on languages. Each individual “French” speaker has a grammar projected by their brain, and these are (generally speaking) sufficiently similar that we can maintain the fiction that they are the same. The only difference I see is that linguists can give a rather explicit account of any given instance of i-French whereas it’s difficult to describe political institutions in similarly detailed terms (though this may just reflect my own ignorance about modern political science). In some sense, this explicitness at the i-language level makes e-French seem even more artificial than e-France.
1-on-1 Zoom
If you’re just doing a “meeting” with one other person located in the same country, I don’t see the point of using Zoom. Ordinary phone lines are more reliable and have more familiar acoustic qualities (this is why VoIP sounds worse: unless you’re quite young, you’re probably far more familiar with the 8kHz sampling rate and whatever compression curve the phone system uses). Just call people on the phone!
Foundation models
It is widely admitted that the use of language in terms like formal language and language model tend to mislead neophytes, since they suggest the common-sense notion (roughly, e-language) rather than the narrow technical sense referring to a set of strings. Scholars at Stanford have been trying to push foundation model as an alternative to what were previously called large language models. But I don’t really like the implication—which I take to be quite salient—that such models ought to serve as the foundation for NLP, AI, whatever. I use large language models in my research, but not that often, and I actually don’t think they have to be part of every practitioner’s toolkit. I can’t help thinking that Stanford is trying to “make fetch happen”.
Stress transcription
I have recently encountered several published works which claim to use IPA-style transcriptions, but mark stress immediately before or after the vowel. This is wrong. The IPA guidelines clearly state that stress is marked at the start of the syllable; it thus acts as an indication of syllable boundary. The more you know…
New-jack Elon haters
It’s on trend to hate on Elon Musk. This bugs me slightly, because I was doing it before it was cool. The thing any new-jack Elon hater should read is the 2015 (authorized!) biography by Ashlee Vance, entitled Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future, which your local library almost certainly has. (There’s even an abridged YA edition.) Basically all the worst things you can imagine about Musk were already firmly documented by 2015. Repeated there is the suggestion that he abused his first wife and more or less purchased his second one. Occasionally, Vance allows Musk to interject things, particularly about who deserves credit for which rocket nozzle, but apparently Musk had “no notes” about these salacious personal life details.
Dialectical vs. dialectal
The adjective dialectical describes ideas reasoned about through dialectic, or the interaction of opposing or contradictory ideas. However, it is often used to in a rather different sense: ‘pertaining to dialects’. For that sense, the more natural word—and here I am being moderately prescriptivist, or at least distinctivist—is dialectal. Dialectical used for this latter sense is, in my opinion, a solecism. This essentially preserves a nice distinction, like the ones between classic and classical and between economic and economical. And certainly there are linguists who have good reason to write about both dialects and dialectics, perhaps even in the same study.
Don’t take money from the John Templeton Foundation
Don’t take money from the John Templeton Foundation. They backed the murderous Chicago School economists, the genocidal architects of the war on Iraq, and are among the largest contributors to the climate change denial movement. That’s all.
Results of the SIGMORPHON 2020 shared task on multilingual grapheme-to-phoneme conversion
The results of the SIGMORPHON 2020 shared task on multilingual grapheme-to-phoneme conversion are now in, and are summarized in our task paper. A couple bullet points:
- Unsurprisingly, the best systems all used some form of ensembling.
- Many of the best teams performed self-training and/or data augmentation experiments, but most of these experiments were performance-negative except in simulated low-resource conditions. Maybe we’ll do a low-resource challenge in a future year.
- LSTMs and transformers are roughly neck-and-neck; one strong submission used a variant of hard monotonic attention.
- Many of the best teams used some kind of pre-processing romanization strategy for Korean, the language with the worst baseline accuracy. We speculate why this helps in the task paper.
- There were some concerns about data quality for three languages (Bulgarian, Georgian, and Lithuanian). We know how to fix them and will do so this summer, if time allows. We may also “re-issue” the challenge data with these fixes.