Well-earned into the present day. I regularly see typos even today.
"People of ethnic group membership can change over time and with age."
They fixed it eventually (though I doubt it had anything to do with my comment).
There was also the following opinion piece, which still makes the utterly absurd claim that Jesse Jackson campaigned for the capitalization of 'African American':
https://www.theguardian.com/commentisfree/2020/oct/21/black-...
(I hope the fact that both of my examples happen to relate somewhat to race doesn't make me sound like an alt right troll. I'm sympathetic to the article. It just seems that there was a major fact checking or editing fail.)
https://www.theguardian.com/gnm-archive/gallery/2016/nov/18/...
But here it seems like a good choice to build on a battle-tested library of regrets, and it's clearly working well for them.
The demo looks slicker than the typical Grammarly/MS Word/native macOS grammar and spelling corrections, for those who missed it: https://www.youtube.com/watch?v=Yl0nb94N98k&feature=emb_imp_...
And the ability to flag false positives, send suggestions back, and see metrics of how the system's being used is just awesome.
Also, I'm a big fan of regex. I think -- probably thanks to jwz's famous quote -- a lot of younger programmers avoid them but they're fantastic for MATCHING. Using them in a Google sheet is a killer MVP to prove out something like this.
I suppose I still use them because I don't know of a better way to do things.
General maintainability is a priority, and we'd like to improve our rule management tooling to make the process of rule maintance generally accessible to editorial staff. We're also working on making noisy rules match more specifically, which usually involves migrating the initial regex into Languagetool for e.g. pattern-matching on part-of-speech.
Thanks sharing these projects, other suggestions are very welcome – we'd be interested in adding new matchers based on different tech if they were a good fit for the use case.
I suspect the biggest problem with using regexes is over-suggestion, trying to correct American English spellings in a quote for example, but I suspect this is a pretty good balance of features, usability, and correctness.
One issue that comes with more complex systems like you mention is that the bugs become more complex. I'd imagine it's fairly easy for a journalist using this tool to know why an incorrect suggestion has been made, and that makes it easy for them to disregard it. While the error rate may improve with more complex analysis, those errors that do still happen are likely to be less understandable.
It's a bit surprising that the engineering blog appears to be embedded in the main site, though. I've worked at a news org in the past (admittedly much larger) and the engineering/meta blogs were entirely separated from the main news section. Obviously it doesn't make sense to reinvent your stack, but I'm surprised the surrounding site scaffolding isn't at least distinct to show this isn't primary news output.
I've always felt automated checks + fixes for grammar and style are miles behind where they should be by now. Checking over and over e.g. long emails for problems before you send them is super time consuming, and that's not even considering help with tone and the overall message.
What does make it interesting is if it were applied as a GPT-2/3 module, and let loose as a reddit comment bot to train a model for engagement and provocation. Editors are essentially model supervisors, and if the object is to provoke and flatter people to sell advertising, it seems more like a compute problem to distill this process into a business.
Human writers creating organic content aren't really necessary for that, and very soon we should be able to generate content and then attribute it to loyal personalities that we stand up as minor celebrities, not unlike the old Hollywood studio system from the early 20th century, where talent was well kept, but still very much kept.
They even have a snippet of Scala code. I feel like HN must be the target audience
- regex rules are updated frequently (let's say weekly)
- the updates are available to hundreds if not thousands of users in different locations
- all of them have the latest ruleset
- all of them capable of sending feedback regarding how useful and correct the suggestions are
- said feedback is analyzed regularly and used to refine the ruleset
The results page generated by the script could have checkboxes to mark each suggestion as useful/not-useful/incorrect and a submit button, with this feedback saved in MySQL.
(I'm not sure whether this qualifies as "without ... services")
Early 21s century -- hopefully there is more to come :D
