Hello, everyone, and welcome back
to the second panel.
And I'm now joined by
Chaohai Ding from the University of Southampton.
Lourdes Moreno from the Universidade
Carlos III de Madrid in Spain.
And Vikas Ashok from
Old Dominion University in the US.
So thank you all for your availability.
It's great to have you here.
And let's as I said before,
let's bring back the topic of natural language processing. We
we addressed it
yesterday, but now from the perspective of
how can it be used to enhance accessible communication on the Web.
And so here and I guess
once again, similar to what I've done
an hour ago in the first panel,
you've been working on different aspects of this
large domain of accessible communication.
And you’ve
pursued advances in machine
translation, in sign language,
AAC, and so from your perspective
and from your focus on the work,
what are the current challenges that that you've been facing?
And that's that are preventing
the next breakthrough, I guess.
And also I would like to ask you to, uh, for your first intervention also to do
a brief introduction to yourself and to what you've been doing.
Okay, So I can start with you Chaohai.
Thank you for having me today.
My name is Chaohai Ding and I'm a senior research fellow at the University of Southampton.
And my research interest is AI and inclusion
which includes using data science and AI
techniques to enhance
accessible learning, traveling and
communication.
So, yes, we used...
NLP has been widely
used in our research to support accessible communication.
Currently, we are working on several projects focused on AAC.
So for example,
we applied the concept
net of ... knowledge graph
to interlinking AAC
symbols from different symbol sets.
This can be used
for symbol to symbol translation.
And we also developed an NLP model to translate
the AAC symbol sequence into spoke text sequence.
So so that's the two projects we're working on currently
and we also working on accessible e-learning project
that we applied a machine translation
to provide transcripts
from English to,
other languages for our international users.
So that's another scenario we are working with on a machine
translation for accessible communication.
So there are a few challenges we have identified in our kind of research.
The first one is always the data,
data availability and the data opti...bility
So as we know, an NLP model is normally trained on a large amount of data. So
especially for AAC,
we, we are... one of the biggest challenges
is that we are lack of a
data like user
user user data... AAC data, and also
how a user interact with the AAC.
So so
which...
and also we have several different
AAC symbol sets used by the different individuals
and which make it very difficult to develop NLP models as well
because the AAC symbols
are separate for each symbol set and that's the
another challenge is the lack of data
interoperability in AAC symbol sets.
Yet the third challenge we are identified is the inclusion
because we are working on AAC symbol sets
from Arabic, English and Chinese.
So there are cultural and social difference in AAC symbols, which is
important to
consider the needs of different end user groups on the cultural and the social
factors, and to be involved in the development
of the NLP models for AAC.
The first one is
data privacy and safety and this has been
identified in our web application for
or from AAC symbols to spoken texts.
So how do we, if we want to, a more accurate
or more specific,
personalized
application?
We need the user's information.
So the challenge is how
how can we store this
personal information
and how how prevent the data misuse and the bridge
and how to make the tradeoff
between the user
information and the the model performance.
And the last one
is always the accessible user interface.
So in the how,
how to makes this AI powered tool, NLP powered
tools accessible for for end users and
and also
there are more generic issues in
AI like accountability Explainability so yes I think
that's the last of the challenges we have identified in our research.
Thank you.
Thank you, Chaohai.
Is a great summary of definitely some of the major challenges
that well are spread across the entire domain.
Definitely. Thank you so much.
Lourdes, do you want to go next?
You’re muted.
Thank you.
Thanks for the invitation.
Good afternoon everyone. I’m Lourdes
Moreno. I work as an associate professor in the computer
science department on the Universidad Carlos III
de Madrid, Spain.
I am an accessibility
expert. I have been working in the area of technology
for disability for 20 years. And...
I have previously worked on
sensory disability but currently
I work on cognitive accessibility.
In my research work I combinate
method from the human computer interaction
and natural language processing areas
to obtain accessible solutions
from the point of view of reability
and the stability of the language in user interfaces.
And so is
the question currently in natural language research is being developed
at our language model in recent years,
there had been many advances
due to the increasing resources
such as large dataset and cloud platform
that allow the training of large models.
But the most crucial factor is the use
of transforming technology and the use of transfer learning.
These are methods based on deep learning to create language model
base of the neural network.
They are universal models,
but then support is different in natural processing
language tasks, such as question answering, translation,
summarization, speech recognition and more.
The most extensively used models are the GPT
from OpenAI, and Bard from Google.
But new and bigger models continually appear
that outperform previous one
because they are a performance continuous to a scale
as more parameters are added to their models and more data are added.
However, and despite these great advance
there are issues
in the accessibility scope challenges to address.
One of them is bias.
Language models have different types of bias
such as gender, race and disability
but gender and race
biases are highly analyzed.
However it is in the case
with disability biases.
It has been a relatively underexplored.
There are studies relative this matter for for example
in in these work in the in the sentiment and analysis text
the terms relative to disability
have a negative value
or in another work using a model to moderate conversation
classified takes mentions of disability
as more toxics.
That is algorithms are trained to be restful
that can be offensive and cause
disadvantage to individual with disabilities.
So, a investigation is necessary
to study in depth models to reduce biases.
We cannot only use these language model and directly
use the outcome.
Another problem with these model
is that there aren’t too many dataset
related to accessibility area.
For instance, there a few label corpora
to be used in training simplification,
algorithms, lexical or syntactic simplification
in natural language processing.
I work in cognitive accessibility in a in Spanish to simplify text
to plain language and easy reading language. To carry out this case
we had to create a corpus with an expert in easy reading,
with the participation of older people
and with people with disability intellectual disabilities
because the current corpora had been created
with non expert in disability, non expert in plain language
and they haven't taken into account the people with disability.
Also an effort devoted to solving this
scarcity of resources are required
in language with low resources.
English is the language with more developed with many natural language processing.
But others, such as Spanish, have hardly
any resources. We need system
trained for English language to work for Spanish as well.
And finally, with the proliferation of GPT models
with application such as ChatGPT
another problem to address
is the regulation on ethical aspect of
artificial intelligence.
Okay, thank you so much.
Lourdes, definitely some very relevant challenges in there.
Vikas, I’ll end this first round with you.
Thank you Carlos. I’m Vikas Ashok
from Old Dominion University in Virginia United States.
So I have been working researching
in the area of accessible computing for like ten years now.
And my specialty focus area is of people with visual disabilities.
So I have mostly concentrated on their accessibility
as well as usability needs when it comes to interacting
with computer applications.
So with the topic at hand, which is accessible communication.
So one of the projects that I'm currently looking at
is understandably of social media content for people
who listen to content such as, you know, people who are blind.
So listening, you know, social media content,
text is not the same as looking at it.
So, even though the social media text
is accessible, it's not necessarily understandable because of presence
of a lot of nonstandard language
content in social media such as Twitter.
Like people create their own words.
They're very inventive there, so they hardly follow any grammar.
So text to speech systems such as those used in screen
readers cannot necessarily pronounce
these out of vocabulary words in the right way,
because most of these words, even though they're in text form,
they're mostly intended for visual consumption, such as some kind of exaggeration
where the letters are duplicated just for some kind of additional effect.
Sometimes even emotions are attached to the text itself without any,
you know, emoticons or anything else.
So and sometimes they try to phonetically match it,
use a different spelling for a word just for fun purposes.
So this kind of fun as communication has increased tremendously on social media
and people are depending on social media to understand or get news, even,
you know, some kind of disaster news or something happens anywhere.
Some even, they first flock to the social media to get it. So
people who listen
to content also should be able to easily understand, so I’m focusing on that area
how to use NLP to make this possible.
Because even though this is not exactly a question
of accessibility in a conventional sense, but it's more like accessibility
in terms of being able to understand the already accessible content.
So that's one of the things.
The other thing that we're looking at, which is related to this panel
is the... related to the bias, disability
bias of natural language models, especially those large language models.
So unfortunately, these models are reflective of the data it's trained on
because most of the data associates words that are used to describe people
with disabilities somehow end up having negative connotation.
So they're using negative context.
So it's nobody's telling these models to learn it that way
except that the documents or the text corpus that these models
are looking at inherently put these words that are,
you know, many times not offensive into the negative category.
So I'm looking at how we can counter
this. One example is toxicity detection
in discussion forums, online discussion forums are very popular.
People go there, sometimes anonymously,
post content, interact with each other.
And, you know, some of the posts get flagged
as, you know, toxic or this get filtered out.
So even if they're not toxic
because of the use of certain words to describe disabilities or something.
So we want to avoid that.
So how do we how can we use NLP to not do that.
So these two projects are pretty much what's closely related to the panel.
So stick to these.
This session.
Thank you, Vikas.
I'll follow up with that with what you've mentioned and Lourdes
has also previously highlighted
the disability bias and... and I'm wondering
if you have any ideas and suggestions on how can
NLP tools
address such issues.
I'm thinking, for instance, text summarization tools,
but also other also NLP tools.
How can they help us address issues of disability bias,
but also how can they explore other aspects
like accountability or personalization
of in the case of
text summaries?
And how can I personalize a summary
for specific audiences for the needs of specific people?
I'll start with you now Lourdes.
OK.
Text summarization is
a natural language processing task.
Is a... is a great resource
because improve cognitive
accessibility in order to help people people with disabilities to process alone
and deduce text.
Also, in the web content accessibility guidelines
following success criteria 3.1.5 Reading Level
the readable summary is a
resource that is recommended.
But these task has challenges.
Such us, bias, disability biasis.
And the summaries generated
are understandable for people with disability is
is at a is understandable for people with disability
therefore some aspects must be taken into account: is necessary
to approach this task which is summarize of the extractive type
where the extracted sentences can be modified with paraphrases
resources and help understandability and readability of the text.
To summarize text different input are required
not only knowledge about the sequences of words or other
leads about sentences,
but also about the target of audience is important.
Different type of user
require different type or personalization of summaries.
It also a
I think that this is
it will be recommendable to include
readability metric in the summary generation process
to ensure that the resulting summary is minimally readable.
For instance
if
we are in the context of a system that provides summaries of public
administration information for old people,
it's necessary to take into account that the summary
must be in plain language,
therefore in addition to extract the relevant sentences
and paraphrases it will be necessary to include knowledge about of guideline
of plain language to make the text easier to read
and finally corpora use
to train natural language processing system shall be tested
with users it in order to attain useful solution.
Only then it will be possible to obtain understandable summaries
for the whole of society and the elderly
and and with respect to accountability
as as in every artificial intelligence algorithm,
these must be explainable
so is necessary to respond to answer
to questions such as how processing
actually perform, the limitation of the dataset
used to train and test algorithms and the outcomes of the model
a therefore good
data manager, management and machine learning models
trained in practice
shall be promote to ensure quality results.
And nothing else.
Thank you Lourdes.
Vikas, do you want to... even though we... from what I understood,
you don't work directly with text summarization but still others
aspects of disability bias, accountability,
personalization, impact what you're doing.
Yeah, I mean I use a lot of text summarization so I can add to it.
So to add to what Lourdes said,
simplification is also as important as summarization
because sometimes it's not just summarizing or shortening the content
to be consumed, but it's also making it understandable, like I said.
So that means that certain complex sentence structures
and some exotic words
we need to replace them with equal and easier
to understand more frequently used words.
So there there is some work there that has been gone into text simplification.
We created some kind of summarization in the special case.
It's from the same language to text from between the same language.
So the input is a text in the same language as the output text,
except that the output text is more readable, more understandable.
So that is extremely important.
The other thing is
summarization.
Most of them tend to rely extractive summarization wherein
they just pick certain sentences from the original piece of text
so that they don't have to worry about the
grammatical correctness and proper sentence structures
so that because they rely on humans
who have written the text in order to generate the summaries.
So I can speak about
how summarization needs to be personalized in certain way for certain groups,
especially for people with visual disabilities.
So what I have noticed in some of my studies is that
even though they can hear it, they don't necessarily understand it
because the writing is sort of visual in the sense
it needs to need you to be visually imaginative.
So what is the alt... the non-visual alternative
for such kind of text?
So how do you summarize the text that includes a lot of visual elements to it.
So how do you convert it into non
equal non-visual explanations?
This necessarily goes beyond the extractive summarization.
You cannot just pick and choose,
so you need to replace the wordings in the sentence.
By other wordings that they can understand and some of the text,
you know, these days, especially
the articles, news articles and all, they don't come purely as text.
They're sort of multi-modal in the sense
there are pictures, there are GIFs everything.
And the text sort of refers to these pictures
so that this is another problem because then it becomes highly visual.
So you have to take some of the visual elements
of the picture, probably through computer vision techniques or something,
and then inject it into the text in order to make it more self-sufficient
and understandable for people who cannot see the images.
So that's my take on it.
Yeah, that's a very good point about the multimedia information
and how do we summarize everything into text.
Yeah, that's a great point.
Chaohai, your take on this?
Oh yes. Yeah.
But we don't know much experience in text summarization.
Most our research is focused on the residual AAC and interlinking
and the AAC generation, but we do how well the project
involved part of the text summarization.
We construct a knowledge graph
for e-learning platform and that we need to
extract
the text summarization from lecture notes to, make it easier
and accessible for people, students
with disabilities. So,
so based on that project,
what we learned is that text summarization is very difficult task
in NLP because these are highly dependent on the text
context domain or
target audience and even the goal summary.
For example, in our scenario, we want to have the summary of
each lecture notes,
but we a very long transcripts in that lecture.
So we use a few
text summarization models to generate
the summaries, but the outcome is not good
somewhere.
It is mainly as
Vikas just said, some of the text
summarization is just pick some of the text and replace
some of the words that say so or even some that doesn't make sense.
So that's the one.
One problem we identified in text summarization
and we also have some
method to
to read
because we we
we need to personalize because the project is
related to the adaptive learning for individual students.
We need a personalization for each student. So
personalization could be
customized, adapted to user need.
But this is actually
can can be improved the ways
users’ personal preferences
or feedback and
and also allow user to set the
summary goal and...
and also the simplification is very important because some students
may have cognitive disability or or other type of disability.
They need to have simplified or blend
plain language. Yet.
Yeah, I think that's mainly what we
have for text summarization.
Thank you, Chaohai.
Thank you. Uh.
Okay, so let's move on to what we started with the challenges
and now I would like to move on to the future perspectives.
How do... what are the breakthroughs that you see happening
promoted by the use of NLP
for accessible communication.
And I'll we'll start with you now, Vikas.
So my
perspective is that there's plenty of NLP,
you know tools out there already that haven’t been exploited
to the fullest extent to address accessibility and usability issues.
The growth in NLP techniques
and methods that has been extremely steep in the recent years
and the rest of us in different fields are trying to catch up.
I mean, still, there is a lot to be explored
as to how they can be used to address
real world accessibility problems,
and we are in the process of doing that, I would say so
text summarization is one thing
that we discussed already, which can be exploited
in a lot of scenarios to improve the efficiency
of computer interaction for people with disabilities.
But the main problem, as we discussed not only in this panel
but also on other panels, is the data.
So for some languages there is enough ... corpus
where the translation is good, because the translation
essentially depends on how much data you have trained on.
But for some pair of languages it may not be that
easy.
Or even if it does something may not be that accurate.
So that may be a problem.
And then the biggest area where I see,
which can be very useful for solving
many accessibility problems is the improvement in dialog systems.
So natural language dialog is more like
a really intuitive interface for many
users, including many people with disabilities.
So those who have physical impairments which
prevent them from conveniently using the keyboard or the mouse
and those are blind who have to use screen readers,
which is time consuming, is known to be time consuming.
So dialog assistants are, I would say
under-explored... they're still exploring it.
We see that commercialization is going on like smartphones and all,
but still it's at the level of some high level interaction
like setting alarms or turning on lights and answering some questions.
But what about using that to interact with applications
in the context of an application?
So if I see a play,
I had a user
comment to this particular document.
It's in word or Google Docs. So can an assistant spoken
dialog assistant understand that an automated
means so this kind of automation
will sort of address, I feel will address many of the issues
that people face interacting with digital content.
So that's one of the things I would say we can use NLP for.
The other thing is the increased availability of large language
models, Pre-trained models like one Lourdes mentioned, like GPT,
which is essentially transformer decoder or generator based model.
Then there's also Bert, which encoder based.
So these help us, you know,
in a way that we don't need large amounts of data to solve problems
because they're already pre-trained on a large amount of data.
So what we would need are kind of small
data sets that are more fine tuned towards the problem we are addressing.
So the datasets, they're accessibility datasets.
They're I think there needs to be a little bit more investment
doesn't have to be that big because the large language models
already take care of most of the language complexity.
It's more like fine tuning
the problem at hand.
So that's where I think some effort should go.
And once we do that, obviously we can fine tune and solve the problems
and then there is a tremendous enhancement or advancement
in transport learning techniques which we can exploit that as well,
in order to not do stuff from scratch, instead borrow
some things that are already there for something different.
I mean, similar problem.
So, so there is a lot to be explored, but we haven't done that yet.
So there's plenty of opportunity
for research using NLP expertise for
problems in accessible communication, especially.
Yes, definitely some exciting avenues there.
So, Chaohai, can we have your take on this?
What are your breakthroughs?
OK.
Just listened them, Vikas, I totally agree with him.
He's
all opinions and
for... for my research
because I've mainly worked on AAC, so, currently,
so I would take AAC for example.
So the future perspective for AAC,
NLP for AAC, I think the first of
that will be the personalized adaptive
communication for each individual, because
each individual has their own communication,
their own way to communicate with each other
and NLP techniques can be used to make
this communication more accessible,
more personalized and adaptive based on their
personal preferences of feedback.
So this can can be used to
for personalize the AAC symbols so
currently, AAC users
they just using some standard AAC symbol set for
their daily communications, so how can we use NLP to
and generic and the generic AI models
to create a more customized personalized AAC symbols
that's which
which you could be have ability to adapt to the
individual's unique cultural and social needs.
I think that's one potentially
contribute to the AAC users.
The second one will be accessible multi
modal communication
because a
that NLP techniques
they have the potential to enhance this
accessible communication by improving
interoperability in training data
and the between the verbal language sign language and that AAC so data
interoperability could provide more high quality training data for this
language with
elastic set and
additionally,
it can provide the ability to translate
different communication models
and to make it more accessible and inclusive.
So in AAC, so we can have
multiple AAC symbol sets can be link
mapped and interlinked by NLP models
and this can be contribute to the translation between the AAC to AAC
and AAC to text, AAC to some language and vice versa.
Yeah, that's the, the the second aspect I think about.
And then the third one is the
AI assistant communication that Vikas
just, just talk about the ChatGPT.
So with this,
this large language model has been trained
by this big companies and
and they have been widely spreading on social media.
So how
how to using this
this trained large
language models incorporated with other applications and then can use it
for a more accessible communication to help people with disabilities.
That's that's another
future
we are looking for.
The last one that I'm going to talk about
is more regarding the AAC because it's quite expensive.
So affordability is very important
and it can be achieved by the NLP
or AI. That's what I mentioned that we are currently looking into
how to turn images into symbols and how to generate
AAC symbols automatically by using
image generative AI models like stable diffusion. So
so that's the another future we are looking forward
how to reduce the cost for accessible communication.
Yeah. Thank you.
Thank you,
Chaohai. Definitely a relevant point.
Reducing costs of getting data and all of that.
That's important everywhere.
So, Lourdes, what are you looking for
in the near future?
And you are muted.
So as we have mentioned before, there are two trends that are the
the appearance of new and better language model than the previous one
working in these these new models
and to reduce the disability biases.
Also I am going to list a specific natural language processing
task and data application
that I will work in the coming year.
And one of them is accessibility to domain specific.
task, such as, health
the the
health language is highly demanded need
but patients have problems understanding
information about their health condition, diagnosis, treatment
and natural language processing method could improve their understanding
of health related documents.
Similar sample appear in legal and financial documents,
the language of administration, e-government...
Current natural language processing technology
that simplifies and summarizes this could help in the roadmap.
And another
line is speech to text
speech to text will be a relevant area
of research in the field of virtual meetings
in order to facilitate accessible, accessible communication by generation
of summaries of meeting as well as minutes
in plain language.
Another topic is the
integration of natural language processing
method into the design
and development of multimedia user interface
is necessary to face accessible accessible communication
from a multi-disciplinary approach between different areas
such as, human computer interaction software engineering
and natural language processing.
Finally, another issue is advancing
application in smart assistant in natural language processing
method to support people with disabilities and the elderly.
Assist them in their daily tasks,
and promote active living.
Okay thank you so much, Lourdes,
and everyone of you for for those perspectives.
I guess we still have 5 minutes
more in this session, so I will risk another question
and I will ask you to to try to to be brief on this one.
But the the need for data
was common across all your interventions.
And if we go back to the previous panel, also,
it was brought up by every every panelist.
So yeah, we need data.
What are your thoughts and
how can we make it easier
and to collect more data
for the specific aspect of accessible communication?
Because, we communicate a lot, right?
Technology has
allowed us and open up
several channels to where we can communicate
even when we're not co-located.
So yeah, every one of us is
different points of the planet and we are communicating right now.
Technology improved those that that possibility a lot.
And however
we always hear this, we need more data.
We can't get data.
So how do you think we can get more data
is and of course we need
the data to train these models, but
can't we also rely on these models to generate data?
So let me just
drop this on you now and
any of you want
to go first?
I can go first. Okay. Yeah. Yeah.
Because we have actually working on open
data four years ago before, I mean
the AI and the data science because when I started my PhD
we working on the open data and there is a open data initiative in UK.
So we want to open our data and government data
and the, and the public transport data and that's
how long working on public transportation with accessibility needs.
So there's a lack of data.
At the beginning of my Ph.D.
so few years later, the still lack of the accessibility information data
on this data.
So the I think the so how can we how is this
I mean, the accessibility area, how can we have such a data
to to have to train our model?
I mean, the first advice, what I used to do
with public transport data is I mapped all
available data
into a larger dataset. That's incurred
a lot of labor work like the cleaning data integration
and all this method to make data available.
That's the first first approach.
The second is we think about how can we
contribute like
a data repository or something
like an image net or word net that we can collaborate to together
to contribute the identified
data related to accessibility research.
I think that that's we can as a community, we can create such a universal
repository or, or
yeah, some kind of data initiative
that we can working on accessibility research.
And the third approach is that definitely
that we can generate the data based on the small data.
We can be using
generative AI model to generate more,
but to do the question is, is that data reliable?
The data to generate generate enough, or is that then the bias?
So yeah, that's my my conclusion.
Thank you.
Yes, exactly.
That's the... the big question mark.
Right.
Is that synthetic data reliable or not, so
Vikas or Lourdes do you want to add something?
So yeah I mean I have used synthetic data
before based
the little bit of real data and in some cases
you can generate synthetic data.
So one of the things I had to do was extract
user comments in documents.
Most of these word processing applications allow you to post comments
to the right for your collaborators to look at and then, you know, address them.
So automatically extracting that I had to generate synthetic data
because obviously you have few documents with collaborative comments.
So the appearance there is like, okay
comments will appear somewhere on the right side, right corner,
which will have some text in it with a few sentences.
So there are some characteristics.
So in those cases we generated synthetic data.
We train the machine learning model.
It was pretty accurate on this data, which was like real data.
So... exploit...
In some cases you can exploit the
way data will appear and then generate the synthetic data.
But in many cases it may not be possible.
Like for the project I mentioned, social media,
where text contains a lot of nonstandard words.
Simply replacing the nonstandard words with synonyms may not do the job
because then you take the fun aspect away from social media.
Like, it should be as fun and entertaining
when you listen to social media text as it is when you look at it.
So. So you have to do some kind of clever,
you know, replacement.
And for that you need some kind of expert human expert going there and
doing that.
So crowdsourcing I think is one way to get data
quickly and it's pretty reliable.
And see, I've seen in the NLP community
like NLP papers that appear in ACL and they rely heavily on the Amazon
Mechanical Turk and other online
incentivized data collection mechanisms.
So that I think is one thing.
The other thing I do know, you know, in my classes
especially, I get the students to help each other out, collect the data
so it doesn't have to be that intensive every day.
If they just even one student collects like ten data points
over the semester, it would be like enough data for a lot of things.
So you know, in each other projects and in the end of the course
pretty much they'll have a lot of data for research. So
you know,
everybody can contribute in a way and students
especially are much more reliable because they are familiar
with the mechanisms how to label collect data.
And also they can understand how things work as well.
So, it’s like a win-win.
Okay, yeah, thanks for that contribution.
Good suggestion.
And, Lourdes, we are really running out of time
but if you still want to intervene,
I can give you a couple of minutes.
Okay.
Only a I think that also
we don't find many we need a few data, but in my vision
is also negative because obtaining
the dataset is expensive.
An in accessible communication, I work in simplification.
these data must be prepared
by the expert in accessibility
is important as these data
is validated by people with disability
and use plain language resources
and then it is a problem
to obtain data with quality.
Okay, thank you so much,
Lourdes.
And thanks a very big thank you to the three of you, Chaohai,
Vikas and Lourdes. It
was a really interesting panel
thank you so much for for your availability and