This paper is also about corpora, but different kinds of corpus that, strangely, digital humanities
has neglected, but then so has humanities generally.
There's very little research done on retranslation corpora in any discipline, and a retranslation
corpus is simply a collection of translations of the same text from different times and
places, and of course there are numerous texts that have been retranslated over and over
again.
On the one hand, there are all the texts that are translated into lots of different languages.
That's not so much what we're interested in as the texts that are translated into some
languages many, many times.
This is a project that began, for me personally, my background is in German literature.
This began for me with an interest that developed in the translations of Othello, Shakespeare's
Othello into German, which actually began with an interest in a particular translation
from 2003, a very controversial translation.
I'm going to explain then, in the first few minutes of this presentation, how I began
working with this material with the multiplicity of different translations.
I've fairly quickly, in the course of a couple of years, collected over 50 different translations
of Othello in German, dating from the 18th century to the present, and began to wonder
about how you can explore the differences between these translations and what they signify
historically.
I'm going to talk about that briefly, and then pass over to Kevin Flanagan and Stefan
Teal-Kevin, who has developed a software architecture for creating and storing and
aligning and segmenting, and hence investigating a retranslation corpus, and Stefan, who's
developed visualization interaction interfaces to make it easy and fun to do that.
What we want to do is to create a kind of resource, which is not just of interest to
researchers, but of public interest.
This is the team, the three of us who are here, there are other people who couldn't
be here, and are funders, and so forth.
This began for me about three years ago, the kind of digital aspect of this, creating a
website, delightedbeauty.org, crowdsourcing translations of just two lines, because as
I was reading through the German translations, I realized that there were two lines in particular
where the differences between the translations were quite symptomatic of what is different
in general about those translations from one another.
So I created this web sourcing site to start to expand this project into other languages,
and without much publicity or anything, this has grown through informal Shakespeare studies
and translation networks to all those languages.
These are the two lines that I'm interested in.
Anybody here who knows Othello, knows Shakespeare, will probably be familiar with them.
They're quite controversial, difficult lines loaded with ideological problems, which present
a great challenge for translators.
The first line is, I won't even try to explain, the second line, far more fair than black.
It's about the blackness of Othello.
Fair is not exactly the opposite of black.
It has a lot of possible meanings and interpretations, which are realized in different ways in productions,
on stage, in dramas, and in translations.
This is a screenshot from the Delighted Beauty website, one of the languages where we got
one of the best responses in the sense of really detailed descriptions.
Back translations into English, that's the lines in red, so all contributors are asked
to give us an English version of the foreign language version.
Who knew that there are four different versions of Othello are available in Tehran bookshops?
Well, there are, and they all translate the lines in subtly different ways, which say
different things about state power, ideas about collective identities, ideas about gender,
kinship, and the family.
I don't speak many languages, but I do have German and French.
This is basically a manual analysis of different choices made by a total of 22 French translators
and 32 German translators, so it's a kind of synchronic comparison of the choices that
translators make for these two key terms, fair and black.
This is just to show that we can start to find interesting intercultural patterns in
the data.
Why is it, we discover questions, why is it that such a large proportion of Germans
choose to translate, choose not to translate black?
In their translations there is no word corresponding to the word black, it's absent, or they use
other terms like dark or ugly.
For white, for fair rather, a very high proportion of German translators choose white, very low
proportion of translators in French do.
If you break down these translators further, and this is methodologically a bit suspect,
but you can divide translators into the famous ones and the obscure ones, and if you do that
you find further differences in the German corpus, but not in the French corpus.
Why is that?
I've got some hypotheses, but I won't detain you with them now.
This is just to illustrate the idea, on a micro-example, that corpora of retranslations
are something that digital humanities could get into and that could interest an awful
lot of readers and text users of various kinds, a list there of just some of the names of
authors or books, works, that one could construct retranslation corpora for and explore, ways
of exploring the interrelatedness of world cultures.
Because retranslation corpora are implicitly already aligned parallel corpora, they lend
themselves to a digital analysis.
We can visualize, amongst many, many things we could do with these corpora, we could visualize
what is it that translators admit things, translators reorder things, translators leave
things out, what do they do when, where, in what genres and so on, and we could visualize
exactly how they translate, for example, which words do they use, and we can relate that
to metadata of various kinds.
We can mine such corpora for linguistics, for cultural studies, for intercultural studies
clearly.
And we can also, and this is the point that we're trying to work towards, the work that
we're doing at the moment, find ways of exploring how, if you analyze a multiplicity of translations,
can we shed new light on the base text, the original text?
Can we find out things through the translations that we wouldn't otherwise know about the
original text?
So in this case, Shakespeare.
What we envisage is translation arrays, which is the software that enables people to upload
and interact with and work with corpora.
We call that translation arrays, and we envisage that as a public knowledge site.
Not just for linguists.
Another thing that we can do, using visualization, using machine translation, and using crowdsourcing,
we can overcome language barriers by asking people, for example, to participate by providing
back translations, and then, if necessary, arguing, of course, about those back translations.
Then we can make a site which is in multiple different languages usable and comprehensible
whatever the user's own language capacities are.
To go back to, I don't know if it's big enough for you to really see, to go back to this
case study that I began with, the couplet.
Some of the more interesting, more startling, more surprising translations with excerpts
from page images from the sources that we use.
So starting at the top, early 19th century, then a bit of a jump to early 20th century.
And I'm summarizing, I've given you a back translation there, and on the right, a summary
of the way that this text presents this duke.
So he comes across the duke, the speaker of this couplet, is a romantic, he's an idealized
sovereign, he's a biological racist, he's a fascist leader in this last one, which is
a typescript from an archive.
Just one more slide of these, the only translation of Othello into German by a woman, womanizes
the couplet in the most remarkable way, that example at the top.
All the others that talk about your son-in-law, this duke in a womanly way refers to a daughter.
So the, I wrote an article about this, I wrote an article about these 30-something translations
of this couplet into German, and I submitted it to Shakespeare Quarterly, which is the
world's most prestigious journal of Shakespeare studies, and they rejected it.
They said, and this was really important, they said, we don't need more information,
we need navigation aids.
And so, that's when I started thinking about digital approaches to this.
And I started playing around with algorithms, algorithmic methods, doing it by hand to find
ways of ranking the translations in terms of their differentness from a kind of average
translation.
And this is what Eddy does, we use what we call Eddy and Viv formula to try and sort
translations or rank them in terms of how distinctive they are.
And Viv then is a way of, we've got a huge collection of translations of a short segment,
you get an Eddy value for each of them, then we aggregate all those Eddy values and project
it back onto the base text and say, so if we get a series of segments of the base text,
we can say, these are being translated more variously than others, and that would be a
way of creating a kind of annotation of the base text, which enables you to read and find
hot spots in it, where translators have been provoked into doing something different.
This is a handmade graph of mapping onto a timeline at the bottom, how distinctive different
translations of this couplet are going down through the decade, and this is kind of close-up
for the second half of the 20th century, divided also into different genres of translation.
So the red ones are published complete works, the blue triangles are theatre scripts, typescripts,
typewritten or PDFs more recently, and the little stars, there's a little cluster at
the bottom, those are study editions.
So these are the three kind of basic genres of, and you can see that there are interesting
patterns emerging there, I won't comment on them in any more detail.
So just to say that this is where I began, with a shelf load of books, basically, and
this is where we are now, delightedbeauty.org, VVV, we kind of finished putting the final
touches, well I say we, those two guys, finished putting the final touches to the website this
morning, this afternoon even, and now Kevin is going to explain how it works.
Hello, I'd hesitate to say there were final touches on the website, but there's certainly
enough to look out for the time being.
So as Tom's suggested, there are sound reasons for believing that corpora of retranslations
could be of a lot of interest in analysing a text, and more specifically, in calculating
the Viv and Eddie metric Tom's described.
Tom very bravely began that by hand, as he mentioned, but needless to say, if you're
trying to have a result, as Tom described, where a base text is being divided into parts
and being compared to the number of versions, then that's not the kind of thing you want
to do by hand, if the text is of any size or if you have any number of versions.
And of course, it's a process that begins by initially splitting the text up and aligning
it with various versions.
There are obviously quite a lot of tools available already for aligning base text and versions
and working with parallel corpora, sometimes with multiple versions.
But in looking at the goals of this project and the longer term goals of laying the groundwork
for the work we'd like to see happen after this project, we took the decision to create
something from scratch that fulfilled a very specific, well, actually quite a generalised
brief for this particular project.
So we created a system which we're calling VVV, which stands for version variation visualisation,
although it's designed to do more than visualise variation statistics.
And I'll give you an overview of what the system is, and then run the software and show
roughly how it can be used for aligning and segmenting, and I'll hand over to Stephen
for going through the visualisations of the data that you can then get out.
So we took the decision to make it a web-based application.
Tom's made the point that we're hoping that this approach to corpora re-translations will
be of interest outside the research community.
So to try and make it as accessible as possible, we wanted to design a web interface that additionally
presented texts in a way that resembled their printed versions using a formatted WYSIWYG
approach, and to allow as many people to have access to the system as possible, obviously
online makes that easier, to allow for, given the potential amount of versions and data
involved, to allow for a crowdsourced, would be the word Tom would use, approach to getting
the alignments of versions together.
We happen to have split the functionality into two parts, there's a data services leg
called EBLR which deals with actually storing the documents and the alignments and the segmentation
and generating the statistics, augmenting the formatted documents with information,
and PRISM is specifically the web front-end we built, but EBLR exposes an API with the
idea being that it will be easy to build other projects on top to use the information
or use the data services.
The documents themselves, the approach to segmenting and then aligning a document, the
segmentation of documents, we took a decision to take an approach similar to that of Katma
where segments are defined as by character offsets within documents essentially, and
those of you who have used Katma will know that that means you have to be very careful
about not changing the document, or at least if you do, making sure the segmentation changes
as well, so with that in mind, and given that the kind of use cases we have in mind here,
in this application, documents and segmentation information and everything else is all stored
in a transactional database with the obvious purpose of trying to ensure the data, the
integrity of the data is preserved within transactions.
It is, as I say, a work in progress.
We have got some results to show, but we're certainly interested in soliciting suggestions
for functionality that might be useful for people.
I just need to switch machines to take a look at the software.
Can we bring up the notes on here?
Thanks.
So, at the moment, the screen you're seeing is the ‑‑ oh, sorry, I thought it was
a ‑‑ that's odd.
We tested this earlier on, and all as well.
Yeah?
Okay.
Jolly good.
Here, this is the homepage of the live website, with some example screens, which I'll skip
over for the moment, but perhaps some of you would like to look at a little later.
If I look at the list of corpora, we've ‑‑ I should explain, this is actually a local
copy of the live website, but it's just the same.
We've got two corpora on this particular server.
I'm going to open the Othello corpus, which has a base text of Act 1, scene 3 of Othello,
a version that Tom's curated, and 30 or so German translations from various periods.
And just to briefly show how ‑‑ what the process of segmenting and aligning is
like for users, I've added a copy of one of the versions we already have on here.
This particular screen you'll notice looks very different from the others, some styling
work yet to be done.
And on the left here, we have the base text, which, as you can see, has already had some
segmentation work done on it, and the same applies to the version on this side.
Segments are demarcated by those little square brackets, which you can toggle on and off,
and these segments already have some type information associated with them.
Here this one is of type stage direction.
This is of type speech, and the speaker name is associated as well.
And you can set up colouring, depending upon that kind of metadata that's associated with
it, and toggle that on and off.
If I just to demonstrate what the segmentation process, if it's done manually, is like, I'm
just going to delete a segment here, and I can create a new one in a number of ways,
one of which is to select some text, and I'm going to say, for the sake of argument, though
this is frankly nonsense, that that particular selection is a term that I'm going to want
to align with my base text.
And there's a colouring rule applied for that.
Those of you who are interested in HTML might be pleased to see that if I choose to create
a segment around a text that spans some HTML tags, then the way that the markup is enriched
with segmentation information doesn't break the HTML.
As some of you know, you can't simply put an open tag here and a closed tag here and
expect it to work, because depending on what tags are in between, you need to be very careful
about how that's done.
So just without spending too much time on the functionality of the interface, I'll just
briefly mention how users can do alignment, which includes some very simple automated
alignment facilities.
There are some very sophisticated ways in which alignment can be automated.
For the moment, we've just got some quite simple ways.
I've put in a filter to show only segments of type speech.
I've selected the first two, and I'm going to align those by pressing that align button.
Having done so, I can press this auto button to request that the system simply steps through
the filtered segments one by one, lining them all up.
Most of those are then aligned correctly, but we'll see that one of them isn't, but
we can correct that and have virtually all of the text aligned automatically.
These first few alignments between speech acts are correct.
I'll nevertheless find a point where I happen to know that the speech acts don't align one
to one.
There's a speech by the Duke of Venice and another by the first senator, and in the translation,
that's been amalgamated.
I can get rid of that alignment and get rid of this alignment, and manually align this
particular pair, and then if I say to it, okay, begin auto from there, it will correct
all the ensuing alignments, taking a second or two.
Although that's a very simple approach to automatic alignment, it covers a number of
text types.
Once that alignment information is in place, and I should specify that Thomas and I have
done a lot of work in aligning particular parts of the text or particular couplets of
interest or terms of interest, but thereafter, it's possible to get some very useful visualizations
from the data.
All right, I won't go into too much detail since time is running out, and also since
you are able to check out the visualizations which I'm about to quickly show and introduce
to you online on the website for which you have seen the URL already.
So delightedbeauty.org slash vvv, and all right, so maybe ... Okay, so from the design perspective,
first of all, it was a really comfortable position for a designer to have such a good
system or to be running on such a good system to have all the manual hard work of collecting
the data, collecting the text, aligning them to be done, and now that we're finally able
to work with the data, to work with the alignment information, it's actually really good so
we can do multiple quick sketches of visualizations and illustrations of potential navigation
results.
And we are putting those up also for discussion, so these things which I'm about to show should
be seen as sketches and things which we are testing out and like to discuss and which
are part of the design process that we undergo together.
So for instance, in this screen here, this is just some minor details of like how trying
some alternatives ways of filtering and navigating through such a corpus which has so many different
versions, and I should be able to like quickly go to any of the versions and have a closer
look at one.
And here, for instance, in this view, which is the sort of like explore view, which is
done with one priority in mind, which is drilling down to text with a number of certain filters
which are important to the text genre, for instance, I can filter by a character and
only the speeches of the Duke in this case will be highlighted to me.
Then I can also filter or sort those segments, and I will be presented with a sorted macro
display of the text itself, and from this I can, you know, click into this macro view
and end up at the text itself and also with the version, with the translation of the text,
the specific version, and it will be aligned in the view.
And this is like what we call for now the explore view.
Yeah, sorry.
There's another one which is focusing more on finding patterns in the data, providing
a high-level overview of what we are collecting, and so for us it's an internal communication
tool more or less to see like what are we doing on the data side, and this allows to
just graphically, without displaying a text, explore the patterns and the structures of
the alignments that Tom has created manually mostly, and so we are able to browse the different
texts and see how they differ in the terms of alignment, and this, for instance, shows
that the speech of Othello in this scene is distributed among multiple speeches on the
version side of things because they are spoken by a chorus.
And the last one, and you're still, again, go to the website and check it out, basically,
the last one shows the width values, the aggregated variation intensity values, as
a sort of underlay or coloring of the text segments, which give a basic feedback on the
variation intensity.
So you see with intensive levels differing in the yellow niches of the text, you can
go on the hunt for segments that are translated highly, variantly, and you are able to see
all the translated segments and also sort them according to eddy values, or if you're
looking for a specific author, then you might be able to find them.
And the number displayed here is the eddy values, or the relative distinctiveness of
the individual version, and this will be expanded, and we'll also have back translations for
which we are using Google Translate to allow non-German audience to basically understand
what we are doing and to give a better impression of what is shown here and why this has a high
variation, or why this might have a high variation.
So these are studies which built a starting point, or which are a starting point for building
a more solid framework for this platform, and yeah, this is the current status.
So thank you very much.
Tom, you still want to see?
It's probably obvious, but we were very keen to try and collaborate with you on the interesting
part of this.
So I'll just say that we're not always getting new courses in the Google app, but we're
not always getting new courses in the Google app, so we're not always getting new courses
in the Google app, so we're not always getting new courses in the Google app, so we're not
always getting new courses in the Google app, so we're not always getting new courses
in the Google app, so we're not always getting new courses in the Google app, so we're not
always getting new courses in the Google app, so we're not always getting new courses in
the Google app, so we're not always getting new courses in the Google app.