NLP is not my main field but still relevant to my work because I often use models and resources from NLP as tools. I'm also personally interested in Linguistics and Languages so I follow related news, sometimes attend NLP conferences and follow people in those fields on Social Media.
It is very concerning how few thought is usually put into linguistic or language characteristics when dealing with these topics. I also rarely see cultural considerations etc. Basically everything is considered as "machine learning will hopefully get this right if having enough data" which is unfortunate (ML is a great tool but the conferences are about language processing).
Another big issue I noticed is that a majority of research only targets or evaluates English texts. In many cases the language is not even specified (although it is clear they use English from figures or examples). I even heard people complaining that work on non-English data is treated as too minor by many reviewers so stuff like that often just gets rejected.
I think this is a really weird development for a field which centers around natural languages.
While I sort-of recognize the emotion you describe in myself, it cannot be ignored that these ignoramuses are simply blowing "traditional" research out of the water in terms of results. That's true across the board, from NLP to image data to computational biology.
It's also a bit simplified to consider it a bifurcation between "traditional" linguists and AI experts entirely ignorant of the discipline. Long before the current wave of AI started, Google liked to hire linguists and computational scientists. These teams probably do have plenty of subject matter experts, but for now they are reaping the low-hanging fruits of the suddenly-improved generic methods. As the marginal improvements are inevitably diminished, subject matter will become more salient again.
I'm a computational biologist by training, and have great appreciation for the often beautiful algorithms, many created in the 70s or 80s and allowing then-spectacular feats of tackling large datasets. Unfortunately, it isn't always obvious how to transfer that knowledge to the new way of doing things.
Yes, the seeming performance of (especially) neural models compared to traditional models is probably the main factor. Although, some voices[1] argue that traditional or much simpler approaches still often do a similar job compared to super over-engineered models, especially when going even slightly beyond an existing target-dataset or task.
I'd argue, that improving the ML models is really the job of ML researchers and should be mainly targeting ML conferences like AAAI (Adv. of AI). In other conferences (directly targeting NLP, CV, Comp. Biology, etc.) it should be the main job to combine those models with the domain-specific characteristics (e.g., language information for NLP) or "traditional" methods to make it an interesting discussion.
I was recently doing reviewing for a multimedia conference and quite a lot of the papers I reviewed were basically pure ML papers. A colleague had the same experience.
The ML papers wouldn't bother me if they included specialists of the targeted domain to address the obvious pitfall. I've analyzed the figures in the blog post and skimmed the paper and both one novelty claim ((2) A single massively multilingual model spanning 109 languages and showing cross-lingual transfer even to zeroshot cases.) and an "explanation" (Such positive language transfer across languages is only possible due to the massively multilingual nature of LaBSE) can be debunked just by looking carefully at the figures like I did in the past hour. The languages on which they test the things are also poorly selected (6 constructed languages, one duplicate and one macro-lang) which shows clear lack of attention to details and poor understanding of some basic linguistics notions. But hey it's an ML paper, it's from Google and it has BERT in the title so get attention and will be cited even if it's half-crap.
Yes, this is exactly my point. NLP is about processing language (which have a century old field dedicated to it) yet the new trend is to totally discard that as a minor details. It's not. It's also fine if people mostly focus on English but then they should be clear about it and not claim to address language in general when they are in fact doing English processing in particular.
I live in Japan. I once had a package delivered from overseas where their printers couldn’t print CJK fonts and thus the whole address resulted in just small empty boxes.
The post office inferred my address from the post code + my name and delivered it correctly. There wasn’t even a (noticeable) delay.
This year all their OSes seem to be riddled with issues at release.
- iOS 13.0 was so bad they released 13.1 in less than 5 days, but even now many things are still hit and miss (with 13.2 in beta)
- watchOS 6.0 is also still pretty bad and not yet fixed (with 6.1 in beta)
- macOS 10.15 GM seems pretty buggy
- Well, I think tvOS 13 is ok?
While the situation might be better for people who use the latest betas, it is still a horrible current user experience for all normal users just updating their devices.
Lots of cross-platform features introduced across these updates (like the new iCloud features and new Reminder apps, etc.) are also in a horrible state.
I'm not sure what their QA team is doing this year but it seems almost everything planned for this Fall would have been better off if pushed back a couple of months. Well, if it weren't for device compatibilities... (the iPhone 11/Watch 5 seemed to be more important than stable software across all their platforms and other devices)
The question is what is Apple doing in their software development? From the outside, it looks like there are glaring issues within their engineering teams.
iOS 11 was a complete disaster and it took an entire OS upgrade cycle (iOS 12) to control the most pressing issues. Apple is constantly releasing wild bugs and after getting burned multiple times now, they still don't seem to tackle this internal problem.
The theory about the iOS 13.0 is that Apple was forced to updated to this buggy iOS version because of Apple Watch release. Watch was shipped with watchOS 6.0 already installed and it requires iOS 13. iOS 13.1 was still not finished and to prevent situation where new watch customers couln'd use it after the purchase - they needed to update as it is.
That I was hinting at in the last sentence. Apple Watch 5 and iPhone 11 came out on the same day and needed watchOS 6 and iOS 13 respectively, so basically, those hardware releases forced the buggy *OS to be released across all platforms and devices.
Comparing iOS 10~13 with macOS 10.12~15 release dates, macOS seems to always come out 7 days after iOS.
This year it has been 12 days after iOS 13.0, instead. Wouldn't really call that much of a push back. It's less than one week behind the usual schedule..
Indeed. I used to use LR Classic on my Mac, but since the new LR got more and more features I switched to using my iPad almost exclusively. I don't think I used LR on my Mac in months.
Isn't a battery replacement just $199, anyway? Might have been the cheaper alternative even without the recall.
I had a similar decision a few weeks ago (contemplating whether to go from a 2014 15" MacBook to a 2019 model because of a faulty battery and touchpad.)
I went to the Genius Bar, they replaced the top-case including the battery, a new keyboard, and a new touchpad, for $199. They also replaced the display for free while they were at it (relating to coating issues,) so I virtually got a new MacBook (from the outside) for $199.
My concern was that there is no clear upgrade path for static websites, if running out of free bandwidth.
All per-site addons focus on different kinds of dynamic features (Forms, Identity, Functions, ...) I suppose a Team account would work, but that also seems like paying for unneeded features.
The support team just linked me to the pricing page.
Most of these smartphones and tablets have homescreens which serve a pretty similar purpose. And people are used to having links to all their favorite apps on it.
It is very concerning how few thought is usually put into linguistic or language characteristics when dealing with these topics. I also rarely see cultural considerations etc. Basically everything is considered as "machine learning will hopefully get this right if having enough data" which is unfortunate (ML is a great tool but the conferences are about language processing).
Another big issue I noticed is that a majority of research only targets or evaluates English texts. In many cases the language is not even specified (although it is clear they use English from figures or examples). I even heard people complaining that work on non-English data is treated as too minor by many reviewers so stuff like that often just gets rejected.
I think this is a really weird development for a field which centers around natural languages.