Kalika Bali The giant leaps in language technology and whos left behind TED
Transcriber:
I’m Kalika Bali,
I’m a linguist by training
and a technologist by profession,
I have worked in academia,
in startups, in small companies
and multinationals for over two decades,
doing research in and building
language technology systems.
My dream is to see technology work
across the language barrier.
As a researcher
at Microsoft Research Labs India
I work in the field of language technology
and speech technology.
And I worry about how
can we make technology accessible
to people across the board,
you know, irrespective
of the language that they speak.
So natural language processing,
artificial intelligence,
speech technology,
these are very big words,
they are buzzwords right now.
Everybody is talking about what exactly
is NLP or natural language processing.
So in very simple terms,
this is the part
of computer science engineering
that makes machines process,
understand and generate natural language,
which is the language that humans speak.
When you are interacting with a bot
trying to book your train tickets
or flight tickets,
when you are speaking to a voice-based
digital assistant in your phone,
it’s natural language processing
that underpins the entire technology
that makes that work.
But how does this work?
How does NLP work?
In a very, very basic way,
it’s about data.
So a huge amount of data
of how actually humans use language
is then processed
by certain algorithms and techniques
that make the machines learn the patterns
of natural language of humans, right?
These days, another buzzword that you
hear a lot about is deep neural networks.
And these are the advanced techniques
that underpin a lot of the NLP stuff
that happens right now.
And I will not go into the details
of how that works,
but the thing that you really
have to understand and keep in mind
is that all of this requires
a humungous amount of data,
natural language data.
If you want a speech system
to converse with you in Gujarati,
the first thing you require
is a lot of data of Gujarati people
speaking to each other
in their own language.
So 2017, Microsoft came up
with a speech recognition system
which was able
to transcribe speech into text
better than a human did.
And this system was trained
on 200 million transcribed words.
In 2018, an English-Chinese
machine translation system
was able to translate
from English to Chinese
as well as any human bilingual could.
And this was trained
on 18 million bilingual sentence pairs.
This is a very, very exciting time
in natural language processing
and in technology as such.
You know, we are seeing science fiction,
which we had read about and watched,
kind of come true
in front of our own eyes.
We are making giant leaps
in technical advancement.
But these giant leaps
are limited to very few languages.
So Monojit Choudhury,
who’s like a very good friend of mine
and a colleague,
he has studied this in some detail
and he has looked at resource distribution
across languages in the world.
And he says that these follow
what is called a power-law distribution,
which essentially means
that there are four languages,
Arabic, Chinese, English and Spanish,
which have the maximum amount
of resources available.
There are another handful of languages
which can also benefit from, you know,
the resources and the technology
that’s available right now.
But there are 90 percent
of the world’s languages
which have no resources
or very little resources available.
This revolution that we are talking about
has essentially bypassed
5,000 languages of the world.
Now, what this means is
that resource-rich languages
have technologies built for them,
so researchers and technologists
get attracted towards them.
They build more technologies for them.
They create more resources.
So it’s like a rich getting richer
kind of a cycle.
And the resource-poor languages stay poor,
there’s no technology for them,
nobody works for them.
And this divide,
digital divide between languages
is ever-expanding
and by implication also the divide
between the communities
that speak these languages is expanding.
So in Microsoft, in Project Ellora,
we aim to bridge this gap.
We are trying to see how can we create
more data by innovative methods,
have more techniques to build technology
without having a lot of resources,
and what are the applications
that can truly benefit these communities.
So at the moment,
this might seem very theoretical,
like what is he talking about,
data and techniques and technology.
So let me give you
a very concrete example here.
I’m a linguist at heart, I love languages,
and that’s what I love talking about.
So let me tell you about a language
that many of you might not know about.
Gondi.
Gondi is a South-Central
Dravidian language.
It is spoken by three million people
in five states of India.
And to put this
in some kind of perspective,
Norwegian is spoken by five million people
and Welsh by a little under a million.
So Gondi is actually a pretty robust
and pretty large community
of the Gond tribals in India.
But by UNESCO’s
Atlas of Languages in Danger,
Gondi is designated vulnerable status.
CGNet Swara is an NGO
that provides a citizen journalism portal
for the Gond community
by making local stories
accessible through mobile phones.
There’s absolutely
no tech support for Gondi.
There is no data available for Gondi,
no resources available for Gondi.
So all content that is created,
moderated and edited is done manually.
Now, under Project Ellora,
what we did was that we
brought together all the stakeholders,
an NGOs like CGNet Swara,
and academic institutions,
like IIIT Naya Raipur,
a not-for-profit
children’s book publisher,
like Pratham Books,
and most importantly,
the speakers of the community.
The Gond tribals themselves
participated in this activity
and for the first time edited
and translated children’s books in Gondi.
We were able to put out 200 books
for the very first time in Gondi,
so that the children had access to stories
and books in their own language.
Another extension of this
was Adivasi Radio,
which was like an app that we built
and developed in Microsoft Research,
and then put out there,
along with our stakeholders,
which takes a Hindi text-to-speech system
and allows it to read out news
and articles provided by CGNet Swara
in Gondi language.
Users can now use this app to read,
watch news and access any information
through text and voice
in their own language.
A very interesting thing is that this app
is now being used to translate –
by the community to translate text
from Hindi to Gondi.
Now, what that will result in
is a lot of parallel data,
that we call parallel data,
that will allow us to build
machine translation systems for Gondi,
which will truly open up a window
for the Gond community to the world.
And what is even more important
is now we know how to do this.
We have the entire pipeline
and we can replicate this for any language
and any language community
which is in a similar situation
as the Gond tribals.
Also education – yes, you know,
information access – yes,
but what about earning a living?
Right? What about – how can we make
these people earn a living
through the digital tools that all of us
just take for granted these days?
Vivek Seshadri,
who’s another researcher at MSR,
and his collaborator, Manu Chopra,
they’ve designed a platform called Karya
for providing digital microtasks
to the underserved communities.
His aim was basically to find a way
to provide a means of dignified labor
to the populations, the rural populations
and the urban poor populations
of this country.
They don’t have access
to all the knowledge
to use the digital platforms
that all of us use every day
without even thinking, right?
But …
Here is a large
literate population
that wants to work, right,
and how can we make this
possible for them?
So Karya is one such way
through which this population
can get on to the digital world
and, you know,
through that find work and do tasks
that can then earn them money.
So we saw this and we thought,
oh, this is wonderful.
We could probably use this
for data collection as well.
So we went to Amale,
which is a small village of 200 people
in the Wada district of Maharashtra
and decided to use Karya
to collect Marathi data.
Now, I know what you are thinking –
I’m sure a lot of Marathi speakers
also in the audience –
that Marathi is not
a low-resource language.
Marathi is definitely
a mainstream language of the country.
But as far as language
technology is concerned,
Marathi is a low-resource language.
So we went to this village
and we had a very successful
data-collection trip.
And, you know,
this village is very remote.
They have no TV, they have no electricity,
they have no mobile signal.
You have to climb a hill
and wave your phone around
if you want to, you know,
use your mobile to call anyone.
So they gave us all this data.
But more than that, they gave us
very valuable lessons in life.
One is this pride in one’s own language.
The people of Amale
were thrilled to be doing this
because they were advancing
their own language by doing this.
The second was the value of community.
Very quickly, this became
a village community effort.
People would gather together in tasks
and do this together as a group.
And the third is
the importance of storytelling.
People of Amale were so starved of content
that in the morning, during the daytime,
they would do recordings
of stories in Karya
and then in the evening
they would gather the entire village
and retell and recount
these stories to the village.
So as scientists, we get so caught up
in the science and technology
part of what we are doing, you know –
which is the next best model to have,
how can we increase
the accuracy of my system,
how can I build
the next best system there is –
that we forget the reason
why we are doing this: the people.
And any successful technology is the one
that keeps the people and the users
up front and center.
And when they start doing that,
we also realize that technology
is probably a very small part of this
and there are other things in the story.
Maybe there are social, cultural
and policy interventions
that are required, as much as technology.
So some time back,
I worked on a project called VideoKheti
that allowed Hindi-speaking
farmers in Central India
to search for agricultural videos
by speaking into a phone-based app.
So we went to Madhya Pradesh
to collect data for this,
and we came back
and we were training our models
and we discovered
we’re getting very bad results.
This is not working.
So we were very confused.
Why is this happening?
So we looked deeper
and deeper into the data
and discovered that, yes,
we had collected data
from what we thought was a very silent,
quiet village in the evening.
But what we hadn’t heard
while we were doing this
was that there was this
constant buzz of night insects, you know?
So throughout the recordings,
we had this “bzz” of the insects,
which was actually distorting our speech.
The second thing was
that when we went there
to kind of test our app in the village,
I and my colleague Indrani Medhi,
who is a very well-regarded
design researcher,
we found that the women
couldn’t pronounce the sanskritized words
that we had for some of the search terms.
So, like …
(speaks Hindi)
Which is like the term
for chemical pesticides, right?
Because we got these terms
from the agricultural extension center
and the women,
even though they are farming,
do not interact with that center at all.
The men do, the women probably
use something much simpler, like …
(speaks Hindi)
Which basically means
killing pests with medicine.
So what I have learned through my journey
and what I would like
to put across to you –
by now, I hope you’ve understood me,
is that there is the majority
of the world’s languages
that require intensive investment
for resource creation
if they are to benefit
from language technology.
And this is unlikely to happen
in a very fast and efficient manner.
So it is extremely important
for us to ensure
that the community derives maximum benefit
from whatever that we are doing
in the language tech area.
And to do this and deliver
a positive social impact
on these communities,
we follow what we call the modified
4-D design thinking methodology.
So the 4-D means:
discover, design, develop and deploy.
So discover the problem
that language technology can solve
for a particular language community.
This observation-led approach
can help allocate resources
where they are most needed,
designed for the users and their language,
understand the diversity
in the linguistic properties
and the languages of the world.
And don’t think,
oh, this is made for English.
Now, how can we just adapt it
for Marathi or for Gondi, right?
Develop rapidly and deploy frequently.
It’s an iterative process
that will help you fail fast
and early failures
will eventually lead to success.
The important thing is to persevere.
Do not give up.
And I remember the story
of these two Aborigine Australian women,
Patricia O’Connor and Ysola Best.
In the mid-90s, they went
to the University of Queensland
and they wanted to learn
their own language, called Yugambeh,
and they were told very bluntly,
“Your language is dead.
It’s been dead for three decades.
You cannot work on this.
Find something else to work on.”
They did not give up.
They went to the community,
they dug up oral memories,
oral traditions, oral literature,
and founded the Yugambeh Museum,
which became the most important cultural
and linguistic center for the language
and its community.
They did not have technology.
They only had their willpower.
Now, with the power of technology,
we can ensure that the next page
is written in Salmi from Finland,
Lillooet from Canada
or Mundari from India.
Thank you.