I assume what is to be considered is the (lack of) content overlap of
articles in different languages in general as of, for example,
which also compares different
language Wikipedias but more in the sense of completeness.
Sounds like interesting work, looking forward to seeing what you come up
All the best,
On 30 August 2017 at 00:13, Leila Zia <leila(a)wikimedia.org> wrote:
On Mon, Aug 28, 2017 at 2:01 AM, Scott Hale <computermacgyver(a)gmail.com>
Do you know of a dataset we can use as ground
truth for aligning
sections of one article in two languages?
This question is super interesting to me. I am not aware of any ground
truth data, but could imagine trying to build some from
[[Template:Translated_page]]. At least on enwiki it has a "section"
parameter that is to be set:
nice! :) Thanks for sharing it. It is definitely worth looking into
it. I did some search across a few languages and the usage of it is
limited, in es, around 600, for example and once you start slice and
dicing it, the labels become too few. but still, we may be able to use
it now or in the future.
As part of the research we are doing to build recommendation systems
that can recommend sections (or templates) for already existing
Wikipedia articles, we are looking at the problem of section alignment
between languages, i.e., given two languages x and y and two version
of article a in these two languages, can an algorithm (with relatively
high accuracy) tell us which section in the article in language x
correspond to which other section in the article in language y?
While I am not aware of research on Wikipedia section alignment per se,
there is a good amount of work on sentence alignment and building
parallel/bilingual corpora that seems relevant to to this [1-4]. I can
imagine an approach that would look for near matches across two Wikipedia
articles in different languages and then examine the distribution of
sentences within sections to see if one or more
sections looked to be
omitted. One challenge is the sub-article problem , which of course
are already familiar. I wonder whether computing
the overlap in article
links a la Omnipedia  and then examining the distribution of these
between sections would work and be much less computationally intensive. I
fear, however, that this could over identify sections further down an
article as missing given (I believe) that article links are often
concentrated towards the beginning of an article.
a side note: we are trying to stay away, as much as possible, from
research/results that rely on NLP techniques as the introduction of
NLP will usually translate relatively quickly to limitations on what
languages our methodologies can scale to.
Thanks, again! :)
 Learning Joint Multilingual Sentence Representations with Neural
Machine Translation. 2017
 Fast and Accurate Sentence Alignment of Bilingual Corpora. 2002.
 Large scale parallel document mining for machine translation. 2010.
 Building Bilingual Parallel Corpora Based on Wikipedia. 2010.
 Problematizing and Addressing the Article-as-Concept Assumption in
 Omnipedia: Bridging the Wikipedia Language Gap. 2012.
Dr Scott Hale
Senior Data Scientist
Oxford Internet Institute, University of Oxford
Turing Fellow, Alan Turing Institute
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