Also:
1. If someone is paid to do captioning and/or categorization work, such as
by a GLAM institution or a Wikimedia affiliate with a budget that supports
this kind of work, then integrating this research into Wikimedia workflows
could significantly increase that person's cost-effectiveness.
2. If volunteers are uploading large quantities of photos, this may make
captioning and categorization much less time consuming and therefore
volunteers may be more likely to do substantial captioning and
categorization work instead of doing the minimum amount of work necessary.
Pine
On Wed, Sep 28, 2016 at 12:19 AM, Jan Dittrich <jan.dittrich(a)wikimedia.de>
wrote:
I find it interesting which impact this could have on
the sense of
achievement for volunteers, if captions are autogenerated or suggested and
them possibly affirmed or corrected.
On one hand one could assume a decreased sense of ownership,
on the other hand, it might be more easier to comment/correct then to
write from scratch and feel much more efficient.
Jan
2016-09-27 23:08 GMT+02:00 Dario Taraborelli <dtaraborelli(a)wikimedia.org>rg>:
I forwarded this separately to internally at WMF
a few days ago. Clearly
– before thinking of building workflows for human contributors to generate
captions or rich descriptors of media files in Commons – we should look at
what's available in terms of off-the-shelf machine learning services and
libraries.
#1 rule of sane citizen science/crowdsourcing projects: don't ask humans
to perform tedious tasks machines are pretty good at, get humans to curate
inputs and outputs of machines instead.
D
On Mon, Sep 26, 2016 at 5:55 PM, Pine W <wiki.pine(a)gmail.com> wrote:
Perhaps of interest: "...We’re making the
latest version of our image
captioning system available as an open source model in TensorFlow."
https://research.googleblog.com/2016/09/show-and-tell-image-
captioning-open.html
Pine
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