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@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@wikimedia.org:
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@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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