Hi Olya, Lucie, and Wikidatans, 

Very interesting projects. And thanks for publishing, Lucie - very helpful!

With regard to Swahili, Arabic (both African languages!) and Esperanto, and leveraging Google Translate / GNMT, I've been looking at this Google GNMT gif  image https://1.bp.blogspot.com/-jwgtcgkgG2o/WDSBrwu9jeI/AAAAAAAABbM/2Eobq-N9_nYeAdeH-sB_NZGbhyoSWgReACLcB/s1600/image01.gif - and wondering how the triplets of the Linked Open Data of Wikidata structured Knowledge Base (KB) would stream through this in multiple smaller languages? 

I couldn't deduce from this paper - https://arxiv.org/pdf/1803.07116.pdf - here, for example ... 

2.1 Encoding the Triples The encoder part of the model is a feed-forward architecture that encodes the set of input triples into a fixed dimensionality vector, which is subsequently used to initialise the decoder. Given a set of un-ordered triples FE = {f1, f2, . . . , fR : fj = (sj , pj , oj )}, where sj , pj and oj are the onehot vector representations of the respective subject, property and object of the j-th triple, we compute an embedding hfj for the j-th triple by forward propagating as follows: hfj = q(Wh[Winsj ;Winpj ;Winoj ]) , (1) hFE = WF[hf1 ; . . . ; hfR−1 ; hfR ] , (2) where hfj is the embedding vector of each triple fj , hFE is a fixed-length vector representation for all the input triples FE. q is a non-linear activation function, [. . . ; . . .] represents vector concatenation. Win,Wh,WF are trainable weight matrices. Unlike (Chisholm et al., 2017), our encoder is agnostic with respect to the order of input triples. As a result, the order of a particular triple fj in the triples set does not change its significance towards the computation of the vector representation of the whole triples set, hFE .

... whether this would address streaming triplets through GNMT?

Would this? And since Swahili, Arabic and Esperanto, are all active languages in - https://translate.google.com/ - no further coding on the GNMT side would be necessary. (I'm curious how best for WUaS to grow small languages not yet in either Wikipedia/Wikidata's 287-301 languages or in GNMT's ~100+ languages?). 
How could your Wikidata / Wikibabel work interface with Google GNMT more fully with time, building on your great Wikidata coding/papers?



On Mon, Jun 18, 2018 at 5:17 AM, Gerard Meijssen <gerard.meijssen@gmail.com> wrote:
On average there is little or no support for subjects that have to do with Africa. When I check the articles for politicians for instance, I find that even current presidents let alone ministers are missing in African Wikipedias. So it is wonderful that there have been projects that deal with gaps but what if there is hardly anything?

What this approach brings us is at least information. Basic information in lists, info boxes maybe an additional line of text.

What we apparently have not done is learn from the Cebuano experience. The biggest issue was not the quality of the new information, it is the integration with Wikidata. Everything is new and it did not link with what we already knew. What we bring in this way is integrated information and as long as data is not saved as an article, the quality provided improves as  Wikidata gains better intel.

If anything, the experience of the Welsh Wikipedia brings us more than gapfinder or tiger editathon because of this is more in line with this approach.

On 18 June 2018 at 13:19, Amir E. Aharoni <amir.aharoni@mail.huji.ac.il> wrote:

2018-06-18 2:12 GMT+03:00 Olya Irzak <oirzak@gmail.com>:
Dear Wikidata community,

We're working on a project called Wikibabel to machine-translate parts of Wikipedia into underserved languages, starting with Swahili.

In hopes that some of our ideas can be helpful to machine translation projects, we wrote a blogpost about how we prioritized which pages to translate, and what categories need a human in the loop:

Rumor has it that the Wikidata community has thought deeply about information access. We'd love your feedback on our work. Please let us know about past / ongoing machine translation related projects so we can learn from & collaborate with them.

I'm not sure how has the Wikidata community think deeply about it.

One project that does something related to what you're doing is GapFinder ( https://www.mediawiki.org/wiki/GapFinder ). As far as I know, the GapFinder frontend is not developed actively, but the recommendation API behind it is being actively maintained and developed, but you should ask the Research team for more info (see https://www.mediawiki.org/wiki/Wikimedia_Research ).

Project Tiger is also doing something similar: https://meta.wikimedia.org/wiki/Project_Tiger_Editathon_2018

As a general comment, displaying machine-translated text in a way that appears that is had been written by humans is misleading and damaging. I don't know any Swahili, but in languages that I can read (Russian, Hebrew, Catalan, Spanish, French, German), the quality of machine translation is at its best good as an aid during writing a translation by a human, and it's never good for actually reading. I also don't understand why do you invest credits into pre-machine-translating articles that people can machine-translate for free, but maybe I'm missing something about how your project works.

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