[Music]
welcome back good evening from
wellington we have greg baker with us
here today greg baker is an entrepreneur
author translator and an internationally
water composer and musician he also
codes a bit he's been running software
that populates the leaftop database
which has the goal of being the largest
lexa canary and is also building a
universal grammar extractor which can
currently inflict a plural from a
singular for 11 of the world's nouns
this talk
is for language geeks and machine
learning nerds no my greg
well some tentative first steps towards
a star trek universal communicator
and i've noticed that a lot of people
have been giving acknowledgements of the
land that they're on at the moment and i
thought it'd be really appropriate to
give an acknowledgement of the languages
of the land that i'm bringing this from
um unfortunately
i can't and that's because
the
water medical's people
were had their culture so completely
destroyed by colonization that we don't
even know
what they called their language
and thus it's called
the sydney language because we don't
know what else to call it
and that's kind of tragic and
when we lose a language we lose
a part of what it is to be human and so
it's rather sad to hear that
we estimate that something like 50 to 90
percent of the languages that are spoken
today
will be dead by the year 2100
and there's a a kind of cycle that we've
seen happen
time and time again that causes
languages to die you start anywhere
around the circle and then
rotate around and after a few iterations
around the language is essentially gone
so i'll just do an example start in the
bottom right hand corner children's
being sent to dominant language schools
so the the wu language
um which is you know one of the the
great connections we have to
middle era china
that kind of died when children were
forced to go to mandarin speaking
schools by the the ccp and so that then
meant that
moving across to the loss of culture
just became a kitchen language a
language that you you hear when you're
at home and your parents speak it but
it's not something that you speak with
your friends or anybody you know and so
the next step around that is you stop
identifying with that minority language
and so the population of people in that
who speak it declines which means people
who only speak that language they can't
communicate with the wider population so
they have poor jobs prospects which then
means that they are all the more keen on
sending their children to schools that
speak the dominant language so they can
get themselves out of that poverty rut
and that is happening all over the world
at the moment uh i'll give you another
one uh chechua this is 10 million
speakers so it's like half the
population of australia spread out
across south america
it's our last connection to the inca
empire it's the language of the empire
as it was sort of evolved
and it's dying
in fact the people
who speak chechua if they're in the
cities they'll probably hide the fact
that they can speak it because of this
you know extreme level of i don't want
to identify with my language and culture
at this point you're probably thinking
this is going to be one of these really
depressing talks
but it's
not because there are two things that
are happening right now
that that are
changing this
where we're breaking the cycle of
language death
in two ways that has never really
happened before
um the first which i'll talk about just
for a few minutes um first is the idea
of a language being associated with
technology
and when that happens when
you can start interacting with a
computer in your own
language
this thing gives you this incredible
sense of power and capability
and then the other half of course is as
we get better machine translation
there's the possibility that this may
also
break the cycle of language death
let me talk about the language
associated with technology and have a
just a quick pitch here
there's a language that i'll use in this
talk it has it goes by three different
names they're subtly different languages
there's the language
called bislama or bislama depending on
where you
grew up
in and that's the language that's spoken
in vanuatu strangely enough it actually
is the word for sea slug
um not many people name their languages
after sea slugs but that's what happened
um or papua new guinea uh it's called
tock pissing there's a couple of tiny
little differences or the solomon
islands is called pidgeon and this is a
language that's only really diverged
from english for about 120 years
uh it derives from
where uh particularly australian
landholders captured a lot of
melanesians force them to work on sugar
cane fields and on on ships so you end
up with this language that has english
grammar but sorry english vocabulary but
melanesian grammar and then we've
watched it diverge
now there's something like five to six
million speakers of
the language that i'll call toc peace in
um mostly in papua new guinea but all
throughout melanesia
and so i decided just for fun that i
would pay bradley and jimmy um to
translate as into localized libra office
into uh bish lama
and um if you want to donate i'm aiming
to raise ten thousand dollars that will
be enough to
um to fully translate it and fully
localize it and the extraordinary effect
that this has because there's a bunch of
people who are not comfortable with
computers not comfortable with english
and it's a double burden when they see
hey i can actually operate in my
language on a computer
it's like
suddenly the world has opened up it's
that it's like that feeling you get when
you first play around with open source
software and you suddenly go hey this is
this is a community this is a culture
this is something different
in previous versions of this talk i used
other languages
which nobody knew nobody will know these
languages either but it's kind of fun
because you can kind of get a sense of
when i sound out some of the words
it'll sort of sound familiar and sound
right
okay so the other thing we have the
other tool in our toolbox is universal
translation
and what we want to do is we want to
have the universal translator from star
trek
now
there's
something that i need to say here
that may be a shock to some people
and that is that
star trek is actually fictional it's
it's not a depiction of reality
uh so we don't actually have to follow
how it works in star trek
uh in the original series the universal
translator worked by scanning the brain
waves of the alien
species and in
enterprise it was hoshi's creation
but either way what we notice is that
the star trek universal translator can
cope with really small languages you
land on a planet and there's only two
people on the planet somehow
the the the crew are able to communicate
with these like
aliens who might be the last of their
species
and that's a key point because
if you just want to translate big
major languages that's easy
um the the process for building a
translator from aged language is
assemble a few million pairs of
translated
documents so like you've got maybe the
proceedings of the european unions has a
good set where you've got
document a document b you know that b is
a direct translation of a it'll be
sentence for sentence translated
so you've got these
um yeah a couple of million
sentence pairs
that you can work with
and as of about 2018 the um hip new
technology is to create a transformer so
deep learning with
intention mechanisms
and
you basically try to get it to predict
the next token that comes out from a
translation i'll do an example of that a
little bit later
and it works and then if you throw in a
speech recognition system you probably
only need a few thousand hours of
correctly transcribed speech and then
you can get sort of in the 90
accuracy for speech recognition
a bit more than that and you can start
getting some really
really accurate models
but the problem is this beginning step
first we assemble a few million pairs of
translated documents
just to get a sense of how big that is
so we'll
in the keynote yesterday you know 7000
lines of code in unix version 6 that i
think
brian kernighan mentioned so the
commentary on that gets you 254 pages
and that
that's a lot of information it's enough
to launch an industry for what is it 50
years now
and over on the right i
took photographs of
[Music]
a large number of quite thick books
actually they're mostly uh bible variant
translations
now a bible translation gets you about
30 000 sentences in your target language
so that's a great start but it's not
sufficient on its own
and so just to give you a sense
in that stack there you can see you know
most of the books are bibles and you get
a sense of how how thick they are
well that's about 1 30th of what you
need translated
in order to
start building a
high level full effective
major language translator
so that stack of books there that's 20
books high
that is not enough
you would need to have more than that
kind of corpus
and when you think about it that's
really hard when you're talking about a
language where
there may be less than a million
speakers
getting that much stuff translated oh by
the way a bible translation which is you
know they've got the process pretty well
down pat it's between five to twenty
person years
multiply that by about 30 years what you
need you're talking about 150 to 600
person years
worth of work to start building a deep
learning based translator
when you're starting to talk about low
resource languages you just laugh at
that and there's no way so you do
whatever dirty tricks you can in order
to extract as much vocabulary for
whatever sources you have
and then step two is learn the rules of
the the rules of the uh
target language's grammar
and then you should be able to
synthetically put together some
sentences
you can skip that step if you want to
make your machine learning model and try
to start generating translations and
then separate that you probably don't
shoot for full speech recognition just
shoot for
recognizers that recognize when a word
is spoken
um so just
did we hear this word or not um and that
gets you sort of a
a starting point on some of these
translation work
well
so given that you can't do a really good
job unless you have
a million translated sentences
what's the best you can do with what
little you have
now if i've managed to get this to work
properly i should be able to switch over
here and i've got
here's the
translation texts of
just what jimmy and bradley have managed
to do so far
you'll notice a few things here
so like we're looking at the save menu
so i'll just put my mouse up here
looking at like the the menu for save
that translates as
save him
or save as turns into save olsum
and you can sort of hear the word save
in there and it's right that's where it
is actually coming from
you'll also see that the ch often
changes into an s so you get instead of
change you get
sin
and or sinis and so you can get like
rename is uh scenes of the name and
it is citizen
um okay so we've got this vocabulary the
in a few places um
bradley and jimmy just sort of freaked
out and said i don't know how to
translate templates it's not a word that
we've ever used in
top piston we we always use the english
word
um
so that was kind of interesting all up
we've got about uh it's about 300 um
texts translated uh you'll notice that
there's big gaps where like hard stuff
like how do you translate these things
they might not exist or they might be
difficult to say or whatever
you also see that in general
toxin is a lot more wordy than english
you tend to have a lot more words to say
the same things than they're just spoken
faster
and so what if you wanted to just
translate a single word
let's say they hadn't done the
translation for the word save but we
wanted to extend our localization and
just grab that
well we can do that
what you do is you start with all the
translation texts that have the word
save in them
and then you look across to top pisin
and you say
what are all the words that appear in
translations okay well there's save them
there's olsum there's naropela there's
wak there's one there's true there's
also this dispeller and we can then ask
the question
what's the probability
that this word or this phrase
is
likely to be the translation of the word
save
so if i now jump over to some code over
here
let's see if i got the right one
um
yep this is the right code
this
surprisingly little infrastructure you
needed in order to make this work
i'm using pandas and
[Music]
numpy because it was convenient
the only thing i'm using out of scipy is
just a binomial test the only thing i'm
using out of nltk so nltk is natural
language toolkit
for python it's
the only thing i'm using it for is
splitting words up the word tokenization
so jumping down through a bit of text i
load in the
text from the
the localization um stuff that the jimmy
and bradley have done
and you can see there's
i've removed save so that i'm not
cheating um save as save a copy close
open and so on
and let's just run through a little bit
more
the box of the the code is actually in
these two functions here the every gram
generator and the possible translation
phrases
so the the every gram generator
basically says you give me a sentence
like you know the quick brown and i'll
return back to you the the quick the
quick brown quick quick brown and brown
because i don't know how many words in
top pissing corresponds to the the word
in english so it might be that there's a
phrase but if i'm translating a single
word it's probably a phrase of of letter
of words that come one after another
so just a
little bit optimization there
so what i can do then is i can say all
the possible translations of the word
save
so these are all the things that i've
seen
where the word save appeared in english
somewhere what are all the words i've
seen on the other side and uh
and all the all the
not just words but phrases that uh that
appeared there so
i think i took i mentioned dispeller
computer when i was
writing when i was reading something out
earlier
and so on there's there's literally
hundreds of them but what we can do is
we can say
and this is the core of the code here
i'm going to count up the number of
times that i saw
word a along with save
the number of times that's that together
the number of times that it appeared in
the english
and then the number of times that it
just generally appears in the whole
corpus
so like the word all for example uh it
marks the accusative case in doctrine so
it's in lots and lots and lots of
sentences
um and
um
so you'd see that in lots of places and
we need to sort of weight it by the fact
that all appears in lots of places so
it's very not likely to be the
translation of the word save
well what happens when we guess the word
save lo and behold it actually works and
the probability is something absolutely
astronomically small it's like 10 to the
minus like 100 or something like that so
of course it just comes up saying
nothing there
and if i ask it to run through
and find for me all the vocabulary that
you possibly can
it does okayish
so
long pella means a long thing and that
was a pretty good translation of the
word length
arg one that seems right spelling is
actually right it's if i'm gonna spell
something um then that's the correct
translation
um kind of amusing um how do you say
click a mouse click you press him long
pressing belong to to a thing
so
some right some actually completely
bonkers wrong
so
i'm just looking at that last one that
doesn't look right to me i actually
don't even know how to say that what
that's actually saying but that's that's
wrong but it got it somewhat right and
if your problem is you just need to get
one word out in your target language
this is not a bad technique it does kind
of work
in fact
as i jump onto my next page my slide if
it's not going to crash on me
i've actually done this on some
very large number of
languages in fact i took
bible translations in 1500 different
languages
and looks like my um
libreoffice session is about to crash
i've just got the spinning beach ball of
death i had to switch over from my linux
box to the mac because there were
problems with my camera that the tech
session bothered complained about so i'm
going to have to kill that and
hopefully
um
restart it so let's
let's um
relaunch and um
hope that it behaves itself
let me just check that i'm sharing my
screen successfully here
it does not look very promising at all
at the moment
it goes up to about
here
and so i did this for like a very large
number of languages and 28 000 cpu hours
later this kind of i won't translate a
single word um you can do it get about
sort of 70 accuracy out of it
but if you want to translate a sentence
now that gets a little bit more
complicated
um i've put together a little github
repo it's not
state-of-the-art or anything
particularly special
but it is how you write a translator
when you're dealing with very low
resource languages
as i said the problem you've got is
if you've got a couple of million
documents you can use deep learning and
that's very accurate but deep learning
machine learning models tend to be very
hungry for data
and you wouldn't dream of trying to do
this with like a hundred translation
texts and expect it to work
but there are other machine learning
algorithms that'll that aren't as
successfully accurate
but can at least get some improvements
over a small amount of data so linear
methods like
a linear
support vector machine or logistic
regression or things like that they can
often cope with really small languages
and give you
like
better than chance translations
so how does this work
though i've got on my left here um i've
got a couple of things translated from
english into
tokyo
so the translation for next page is
page tumblr the adjective saying next
happens after the noun
it's kind of flexible actually in um
intoxication you can sort of be a bit
rough on that
in this case uh the translators have
chosen to put the noun first in both
cases first page
page festpeller
so festpeller like you can actually
almost hear first fellow which is where
it actually comes from
and then finally with um
painim what's going on there is
p
often substitutes for the letter f
so
hearing the english word find turned
into pain
and then the d got dropped off and then
the im is melanesian grammar saying um
you know this is a transitive verb
right so given those what do i set my
machine learning model up to do
well the first thing i want to do is i
want to get the first word of
translation out so i set up a training
set and my training set says
if you see
next and page please emit page if you
see first and page please emit page and
if you see search and four then please
emit painting
then
i then have a second machine learning
model or if i'm using a transformer i
can actually like incorporate that into
the model itself that says i'm going to
give you some english words and
one
top piece in word so next page and page
please give me tableau
if you see first page and page please
give me festpeller if you see search and
for and painting that's it end there's
no more
words coming you have completely
translated that particular bit
and
i've got that coded up over here again
same sort of beginning
i went through a variety of different
scikit-learn
options to try and get a good
model that worked
reading the text as as per usual
and i just decided to work with the
shorter texts um to make the the talk
not take like three hours in training
and so these are the
the phrases that it knows about that
have been translated by human being and
our goal here is
could we translate some other terms into
toxin that it hasn't seen
by learning the rules that were used to
what were the simplest set of rules that
would have got you from english to top
this in if we apply those rules to some
unseen texts how do you go
now after a little bit of playing around
i found that
a random forest classifier happened to
be the the best option i tried a few
it's a good compromise it can cope with
quite small data sets and still get
reasonable accuracy and it can still
sort of kind of do that
the
universal function approximations that
is so useful out of deep learning
now this make model function
is doing two things at once
the first part is the
the for loop here where oops zoomed past
really quickly
where what it's doing is it's setting up
the training set
you tell it
take the english sentence and
three top piss inwards and then
make a data frame where the fourth word
is the next column
and
you can see down here and if there's no
more tokens then respond with the word
in
then afterwards it
does some kind of word embedding take
those english words and the top piss in
words
make some sort of
vector that we can use for machine
learning
and i'm being really really lazy here
i'm just using a count vectorizer
and
make a model
from it
and then i've got another function to
make multiple models so make the model
where you've got no top piece in words
make the model when you've got one top
piece in word make the model when you've
got two
and keep on going until you get to about
i think it's about 19 words which is
yeah there was one time where a two
words in english turned into something
like
i can't remember what the actual number
is maybe 14 different top pissing words
in one sentence
hopefully that's not going to be sitting
in a menu somewhere because that's not
going to fit real well so here's the
kind of output there it's saying
if i've got the english words and then
12 top pissing words
then the thing to predict would
be killing up with those 12 or with
these 12 you should be predicting is
east up i think to be somewhere
um
and
so let's let's build a translation model
in real time the fun part about these
tiny vocabularies is that this training
step takes 1.18 seconds
and even that's a little bit slow i've
only got like about 200 words of
um 200 phrases that i'm translating here
so
and it's not
using 100 gpus on a parallel set of
machines for a couple of
weeks then the flip side is i need to be
able to suggest the next token so i've
got a function that will take
the english sentence the tokens that
we've output so far
and then suggest what the next token is
and then a function called suggest
translation which takes
all the tokens
one at a time so i've got my english
sentence gets the first token then
calls next token again with the english
sentence in that first token and then
calls it again with the english sentence
and the first two tokens until it gets
to an end
okay so let's just see how that works um
what's the if i ask you to translate
last page now it's never seen that
before
it's seen the word last and it's seen
the word page
and it's smart enough to say
invert the word order like it's not just
trying to translate last it translates
page because that's what it should do
and then i say okay well if you were
translating last page and i gave you
page what would you admit next okay i'll
admit untuck and then if i
was translating last page and i gave you
page and untucked what would the next
token be and the answer is that that's
the end of the phrase
it did pretty well
on this actually now untuck that's
coming from the english
on top of
so it's like the page that's on top of
the one that you just have
which actually means previous
um but what's going on here is i think
it was like the only other time it saw
the word last was like the last document
that you had open and so that got
translated as
a computer
walk on top the
last as in the the one that was on top
of where you were before
still it did pretty well so it's getting
the sense of the last page as in the
page that was before the one that you've
got at the moment
or if i put the whole thing together
then it'll say
page on top because it grabs all the
tokens
or
next page
notice that it it knows that
it can be the other way around so tumblr
can appear before the noun and so it's
saying well seeing next page what i
think you probably want to have is
page the noun second
and it's it's it's able to understand
that kind of word order noun then
adjective or adjective then noun
depending on the context
which is pretty impressive machine
learning given we've got such a tiny
vocabulary
as we add more vocabulary this will then
get smarter and its ability to predict
translations will be better and maybe
it'll learn that
last has a couple of different meanings
and so then it would be able to suggest
something other than unpuppet it could
do it's like a finis which is final
popping back to my slides here and
hoping it doesn't crash again
and as we sort of head down for the last
15 minutes of the talk i've got to sort
of have my warning scary part
because now we need to talk about
grammar and about number theory
which is kind of an unusual place to to
go
one of the things that we need when
we're localizing programs into languages
one of the really really key useful
things is to know how plurals work
it's really hard to not have a plural
one document two documents
you have many documents open you have
one document open
and in english there's some pretty
simple rules generally you add the
letter s so cat goes to cats dog goes
dogs
except if it ends in a vowel
but always ends in a y with the y you
have sky goes to skies and butterfly
goes to butterflies and so on
and then there are irregular nouns so
person goes to people
sheep goes to sheep
ox goes to oxen she's kind of weird and
this is linuxconf so unix goes to unix
and i have many unix boxes or i have
many unix boxing
i've heard i heard a plural for docker
yesterday which i really liked sort of
doctrine
i have mini docker containers so
therefore i have doctrine
i hope that sort of catches on gets into
the dictionary soon
sorry one more thing on that and there's
another principle of linguistics that
it's basically says no matter how stupid
or weird or complicated
some grammar rule is in the language
that you're studying so like person goes
to people as a plural no matter how
complicated it is
the person next to you their language
has a rule that's even more complicated
and more ridiculous
which is interesting
so what we're trying to do here is
essentially we're making a machine
learning model where i feed in the
singular
and out comes the plural
and with a bit of staring at this it's
actually pretty linear
it's locally linear for certain kinds of
different words so
the rule that gets you from cat to cats
is the same rule that gets you from dog
to dogs
the only problem is that the residuals
can be infinite what by that i mean
if i plotted
person on the singular here and then
draw a spot for
people as the plural they're going to be
way way off so that's going to ruin my
line if i try to minimize the sum of
least squares distance on this line
i'm going to end up in serious trouble
it's it's not going to produce correct
results ever
and here's where i get to proudly talk
about some of my research
um
kurt hensel in 1897 came up with this
idea of periodic numbers and to
completely paraphrase him
two numbers are really close together if
they end with the same sequence of bits
this is a perfectly valid metric space
it's just as good as the euclidean
distance that you learned at school
there's a triangle inequality there's
infinitesimals there you can do calculus
it's very very strange
so for example 3 and 5
they're very close because if you write
them out 3 is
zero one one and five is one one
sorry three is
one one and five is one zero one
so they're the same in the last
bit they both have a one at the end so
they're very close
if you write 10 and 18 out in binary
they end up very close together
as well in fact the last three bits are
the same it's 0 1 0 is the last three
bits so they're very very close
or two numbers that are really really
really spectacularly close uh one and
sixty five thousand five hundred thirty
seven um sixty five thousand five
hundred thirty six is two to the 16
which means you have a 1 and then 15
zeros and then a 1
and that means that the last
the final 1 and the 15 zeros before it
are in common so those are really really
really close together
so big deep breath how it all ties
together
i take my
words and i just take the utf 8 encoding
of them
and i use hensel's measure of distance
and then sky and butterfly are really
close together
because the last eight bits
of sky is letter y
and that's the same as the last eight
bits of butterfly
great i've got eight bits similar
they're really really close together now
dog and frog they're unbelievably close
together because they got the last 16
bits of their representation
so that means i can now do a linear
regression
using those numbers just using the utf-8
encoding
and instead of minimizing the sum of
least squares
i'm minimizing the sum of this really
weird formula which is 2 to the power of
negative
the number of bits that were in common
between the real answer and the fake
answer in other words if my line
predicted that the plural of
dog
was frogs
when the true answer is dogs that would
be pretty close it would say well i've
got ogs
is the same that's 24 bits so that only
counts 2 to the minus 24 in terms of
penalty it's so close
um so then uh dramatic drumroll adding
lots of time um and lots of research so
one of the things i proved last year is
that you can actually
solve these problems in a finite length
of time and then
uh to remember how to program in haskell
because like the whole point of academia
is to look as though you're trying to be
smart and
programming in haskell is how you look
really smart so i presume that works and
then open sourcing it because who on
earth would commercialize it and just
dropped off on that text there running
it on another 1500 languages for another
30 000 hours of compute time i get
some interesting results
so i'm hiding half what i'm talking
about here
but looking at say latin
one of the rules that came out was the
plural is 256 cubed
um times x plus the letters for nes and
the plurals in uh bishomer and top
pissing
was all times 256 to the power 4 plus x
and if i look at the actual text there
um
rule 4 there on on latin cogitatio goes
to cogitationis
cogitatio
multiply it by 256 cubed and that'll
shift you three spaces to the left
add in the ness on the end
and these
translation rules are
um
linear
regression problems
and with bislama the same kind of thing
happens just add the word all at the
beginning and
it's a nice simple linear regression
problem and linear regression problems
are great because you don't need a lot
of data for them and what do you know i
have a very large number of languages
for which we don't have much data
which mean
as in the sort of preview there yes i
can train i can um come up with the
correct pluralization for 11 percent of
the world's nouns
this is not fabulous um my ability to
use this is limited firstly by
the inaccuracy of the vocabulary
extraction so back earlier when it said
it was about right 70 of the time when
we're pulling out individual words
and then this technique for identifying
singulars and plurals and how the
grammar rules work
uh that kind of works
um you can see if you if you're
colorblind there's some green here
if you
are looking at this on a grayscale
monitor probably can't see it but the
little green line here is the
pediatric linear algorithm that i
developed
and
generally it's sort of right 11 of the
time is pretty confident
sometimes it gets up to 30 correct and
very very occasionally it gets sort of
60 or 70 percent
um correct
um
pluralization so we're a long way from
being able to make universal translator
but
according to star trek that only happens
in 2155 anyway so
i've still got 130 years of improvements
on this
but this is what you can do even on the
barest smallest languages so
this ran on um
of
i'm sure i'm going to pronounce it
correctly
which is a language of
arnhem land for example where there's a
few hundred speakers it's it's a very
very sparse language i did this also on
some languages where like it's in the
last stages of extinction where the only
people who who can speak the language
are in their 60s or 70s
there's a lot of work to be done
i've put up some urls there if you want
to contribute or
just play around with it
i still haven't got very far in terms of
synthesizing sentences so
until i can do that
i'm constrained on my ability to use
very parsimonious machine learning
models which really really limits the
accuracy of the translations
um and
i need to work on better embeddings for
these low resource languages there are
some techniques that have just come out
where you know with a few tens of
thousands of texts you can do a sentence
embedding that you can then improve on
just using monolingual text that's a bit
better that's got a bit more hope
kind of hanging out for the open source
speech to text engines
mozilla
and their common voice project they're
making some progress on that and i'm
pretty confident we'll see some some
good results there and i'm also really
hanging out for some alien races for us
to communicate with so we can see
whether these techniques are just human
or whether they're actually universal to
language itself
now given that i've
kind of nearly run out of time and i'm
talking about communicating with alien
races it's probably a good point to stop
there so
how about i
close off the presentation and let's see
if there are any questions
[Music]
well it was really good it reminded me
why i took social linguistics in third
year rather than syntax and
computational linguistics um
we have one question we've got just
enough time
to go through
um
there was a bunch about putting the
links in so as i said we have a bit
rushed for time we'll get that into the
post
post channel room straight after this
talk
but the question is
what
when you have such a small and
distributed speaker base does it does it
does it help or hinder to fix specific
word meanings
when you've got
a small
corpus to to kind of run through
yeah if you've only got a very small
vocabulary then anything you can do to
improve it is good
so
for example
um
if i feed back
um
look last page comes out as page on top
could you give me a translation for a
correct translation for last page
then yeah that should make a big
difference on my um accuracy and so
that's the process of i'm finding as
many texts i can
where it's sort of getting it nearly
right and then we can improve each of
those as it goes along
great um we will get those links to you
um i see there's more more questions
coming in about links um i'm just
checking to see there's no final
questions well it's like i could pop up
the screen or something like that with
the last couple of links on it yeah
we'll be we'll get them into the chat
for sure um okay we'll harvest them from
you in the in the in the post chat in a
few minutes and then get them to the
eager participants
yep
cool i think that's pretty much us so
thank you once again greg um
okay
we'll see you back here in 10 minutes
also