so thank very much for the introduction
i'm going to talk about our recent work
at cambridge university
current dialogue models
are limited in the complexity of dialogue structure as they allow for example relations between
objects
i'm that'll work
we
raquel
we present work as well
so it all a
deliberately break those limitations
and
we're not all of the current to have complex relations between a
complex data structures but you don't x
and even more so we i name system to address
relations between objects and we allow
users to address relations between objects
and even though this model has been
developed for some dp based task oriented spoken dialogue systems
it should be applicable for to all kinds of data systems by you want to
have a symbolic representation of the dialog state
at first the creek we can on and if you based
spoken dialogue systems
so what is here is the architecture of a spoken dialogue system
we have the input processing
modules which yields a semantic representation of the
of the input of the user
it is then used in the belief tracking
module to update the
the belief state which is then used by the dialogue policy to use the next
system action
which is then
transfer back
by the language generation and synthesis module
two response or you response the user
and the task of each tracker is then to update the
the distribution of all possible dialog state based on the current user input the previous
system action and the previously stated
and
you using force them to find optimal policy which maps the current belief state index
system action using a reward signal from the user
and the optimal policy is then defined as
a policy which maximizes the expected future calculated reward
this architecture has been used for
so you go domain dialogues a lot
and also for multi domain dialogues
and
using the multi-domain dialogue model in psychology
here's an example dialog
about finding a little interested in cambridge
so
you saw what does one soldier in cambridge use us a few questions about that
price of instances are aligned and the system table two
give a response
k is right cambridge is expensive fortune the sensor
he's is quite a also on the restaurant in cambridge
in the centre
system continues asking questions to figure out what the actual
the rest of the lexicon meta-file the rest from the user wants and then it
is okay chromacity trying to system
in the sense
and
in the multi-domain dialogue model
the user input first n is the main tracker which you can start okay what's
the current domain of the of the dialog the user input
this case hotel restaurant
and then they are
independent
part of the dialog pipeline which handles than the request buddies
and this model has several as a dialog state
stuff that all
the communication between those two domains
or at least doesn't allow easy
way of doing this type of communication
so for example the user would say trade it also effective referent
in the same area which is a relation relating to information about hotel this was
would not be something this type of
a lot of attention
so instead of
of modeling domain
we are looking at entities that
so
the same
example dialogue
in the beginning
you the dialogue was about
a not sect of type hotel
and then later about object of interest ones
and
also and then entities in our dialogue topic entities
and
that are also contains an additional ldc the relation which is modeled as a separate
entity connecting
both
a text holes in the restaurant
and this is
the main idea of the new dialogue model
so a and all these all these energies countryside centre conversation world
which kind of wine sent again
so i will
go in more detail on but let me tell you more details about those the
dialogue model
and then either so you present your prototype implementation a showcase that we focus on
relation modeling
if you can so that
this has dialogue model
is actually very effective in handling relation
between opt
okay but first we talk about the conversation into the dialogue model in more detail
what are optics tex mex and its use of the real world
we have an object in cambodia the extra restaurant
and all the objects in heaven the matching
and you know database
and based on these entries
actual objects in our conversational world
created
and
of course to notice do that the type of that
also has to match
the database specification
and a relation then connects to objects
no example the restaurant again
and a different definition of this relation can also be derived from
the type definitions of those objects
so in this example
we just compared compatible attributes like area areas
present in both
it would list of the
object type
so this is
this can be something they can connect on for this is part of the relation
to finish
of course you can elaborate is if you
she one
and
each conversation entity which can be adopted or relation maintains a user goal belief state
and the context eight
so whether it's an object or a simple all have their own
own state definition
and the user goal belief represents information
shared by the user bits
as a set of uncertainty whether and
so it's not as prepared probability distribution over all possible user goal states
as stated she's
similar to what we already know
from completely based dialogue modeling
in general
and the connect state represents information which is offered by the system
so for example the system says
well
on the houses a chinese restaurant in the sensor
and this would be something which would be in the context and so the system
knows that it has access information does not uncertainty associated to that
and the clicks it is
very important because
braces of refer to the information we just been shown by the system instead of
and so the information
rather than a computer opticals been found yet
and it is it of conversational world and their entities
allows them to the belief tracking on two levels
on the word level there you can also have a behavioural
a system behavior for creating and entity disambiguation
and then on the energy level they actually
for about what specific to gonna can forward
protesters this property stuff this relation actually have
okay so
do not confuse you anymore i have as a small example but we can go
through step by step
the sex on the t c them before so shouldn't be too difficult the beginning
the user says okay and looking for hosting cambridge whether the system can infer okay
that must be an object
of type or tell
and we don't have any information about it yet
then the other continues there's more information share
and that should be the price range expensive so we cannot tell the user goal
belief state and the system share
the most video what the proposed
and so what cambridge's an expensive one
so this is when the context it is updated
then a user says greater also need cheap restaurant in the scenario
which allows the dialogue system to infer that there's another object of time restaurant
and that's a relation between a topic
so this is when this could come to live if you will
the data continues personal information acquired about the
but it's fun and two
system proposes a computer us plan to the user
which is when the context and the of the object and the relation is updated
so
if you to the properties of this of this
you dialogue model
and compared to the most of them are not only can see on several expected
it is more or flexible and the
so for example objects can be can be addressed by the use of the system
is something
also in common in a way but this new model also allows
to have multiple object of the same time so you could have put model dialogue
where you have
two hotels
all
three best friends or whatever you think of
and were to be able to maintain a dialog states
correctly and you could also have relations between those object even
it also allows the type hierarchy so
the kids okay the rest when the venue and these things and this might have
given you wanted to policy modeling for example
and
well then let the calm assessment of the dialogue model also can do is modeled
relation
it's can be addressed by using the system
poses al
state it the relation to have their own state
this allows the system to actually talk about those relations and address time in a
system response
and also the relations remain specified
even if the context of the object
change or if there's
no object context is if i don't all
so if i if you think of the dialog the user but at the beginning
i'm looking for a whole to and the rest and in the same area
there is no connected all the scenario wouldn't
couldn't be derived any and
in any other way then
like doing in the like having like this separate entities modeling the rest
so this is
well i'm going to tell you about the
how about how the model
where
how to continue the how to cook something to treat example of how we have
implemented this model
and
for experiments on
relation learning something that this is very powerful tool to actually
do this
so one idea of formal beyond us to do when we do implement a portable
to focus on relation labeling and to reuse
the bound on the conditions you already have for belief imposing modeling
we have from the domain based models
so we have a fixed position world
maybe africa will one object of each time
and there's only one conversational taking the focus of attention
which is which helps the system to figure out how to continue the dialogue
and is similar to the to the
the main tracking problem
the user goal belief is more of the marginal distribution for slot
and this is
but also has the effect that you can reuse
step something on the conditions for quality modeling for example but also
but the only possibly relation you can
actually model is the constellation this type of
from you state
another to open questions
but dialogue management
how can be incorporated the conversational objects and relations into the decision making process
and how can we derive an input stage
to the dialogue policies from the conversational entities
and
we implemented a few reinforcement learning approach for that
because this is very
useful as it
have hierarchical structure and it kind of
subdivide the probably the several steps
in the beginning in the based on the focus of attention an object is basically
a
selected whether it's also respond and then there's a master policy a basis master policy
a decision is made but also talk about the art techniques to talk about a
relation place of that object
only after that the system
and the second bullet is able
about ready to the object
or to the relation
so this means that each conversation and he has the all and policy
and is also allows to say a relation policies
because this policy is the same value
come from this
so i talking about a the whole circle the output from the start talking about
the rest
and information we use
two
to
for the policy as input to do this
is on the entity level
for the relation we just look at the state of the relation to but for
you all checked we have to find a way to combine those things
which is necessary to database access basically
and the last odyssey it also we need a combination of the objects in the
relation
as you want to do computer solution
because
the object you please state of the object
could state something which is
it can stick to what the relation says based on what object to have a
specified
and to figure out those conflicts and to allow the system to handle this situation
you need to combine them
we consummate experiments
of finding out and the rest and in cambridge
for which we use the pilot system
and the conversational world and in the six and experiments
consisted of two objects multiple do not have restaurant
and
user goals contain the rage
always
between the slot area pricerange so
every user goal had this relation but was not always use
and there was an additional parameter are which specify the property that the usual you
use a similar to actually use that relation
so the you using lattices and has may consist of a lot to talk about
the value of all that's also various sensor all the talk about the relation area
of course that you "'cause" every restaurant
and if are outside only use a simulated user would say area
every hold because every restaurant
and we'll of the two experiments
comparing the
conversational entity dialogue model with them multi-domain dialogue model
in the first we were interested of the effect of r
does this happen in fact we actually you need to model relation can you can
be able to cope with this problem different way
for this at a fixed all of conversational czech only the second one was learned
and then we also ones of you got to see what the actual effect a
of this type of model on the on the learning behaviour in general
so we sell
experiment it's in experiment one about the unknown to different semantic error level zero percent
fifteen percent
you see the success rate
and the other relation probability are
and
for c represent you could clearly see that with the high our
the composition is the other one of the form to go if you significantly higher
and then the multi-domain dialogue model
and this is also true for the fifteen percent
case the goddess much lower here because of how the
our remote operations
it is artificial because assimilated but based on that could call some of the
problems you would usually have
in this situation
note interesting here is not only that the that the concession digit our model
performs better
for high are also that follow lost it does not
so the model domain data model is able to cope with
with use a talk about relation to a certain extent
touched by basically repeating the question
so you looking for both what prices are looking for
yes n is also a classifier for
t
if it does restate a question
this is the model can learn all the call this
i'm going to a certain extent and on in terms of success rate or re
well which
goes the we want
but i would assume that if you look good
user to sex anything's it might be different
and
we also looked at how many of the system actions actually address
and
how many i'd how mean dialogue system the system actually interest
and the relation and then we the system utterances in twenty four point five dialogues
for twenty four point five percent of the dialog directly address a relation which i
think it is very interesting
and for experiment to a we have a similar situation
it's not the strong because they took turns and effects of the domain
if the rest know how to most part of the first
first object of the dialogue was about then it
there was a relation present
so
i think we can say that the new model breaks up the limitations of current
our models
and
i think the not next logical step towards more complex dialogs
a lot also more complex data structures like relations between objects
and that could shown approach have implementation which we use as normal voice models that
it outperforms the baseline
and the future work
we further need to increase
the complexity of dialogue structures
so what's more complex relation
and
what ended if it had to be ways and just to name only two
so you can you can find implementation on the website
and also we have a new meetings from high to a so if you just
that if you to assign a thank you very much
okay
now we have time for questions that one
so you mean if it if there is like
just multiple talk about multiple objects
and you say the for the third object or whatever this have been the same
area
and then difficulties with this rule a strong gonna
okay
and i mean this depends on how the ontology is modeled
i mean in this example dialogues we
have you have a simple way of modeling the area which is not so please
press centre
i think
for doing these things you would have to
have a different ontology is gifted different
of locating these things
and then you could if you model that i love this situation
also so you so you mean about the relation refers to the to what the
user request of the actual without loss
okay
i mean it's setup currently
and they said to try to go back to that slide
i said relations of references a context a feature space is what the system proposed
so whatever specified for by user
is not necessary take into account when you look at the relation
the relation looks at what the system
also there's an entity whatever the values
it's basically means if you haven't talk about the area at all
then you said i want something the same area then you would be able to
refer to that while the
system offered to the user as a result it is basically what you're saying
you want something which relates to what the system of a global the users admin
in general terms
and i think this is why it it's important to have this as two separate
the possible state
okay so we have a question i think you where the first
i mean this
so the idea of this one is to estimate the model information just circumvention values
of theirs and frequency about that
then a
you have to add things to the model a question is whether the model can
that is actually able to model these things and allows the system to address these
type can be greedy
and this was the goal of this i mean
it cannot solve everything because they would that just have some
some
the reasoning going on maybe or whatever out to be derived information
or you could add some something to the language
to the understanding component or whatever to do this type of thing
if you and but if the system would be unsure about what the correct way
of getting the situation would be you put
the model could the policy learnt how to handle that and i and aspect of
whatever
okay let's move the source it's you
yes
yes
well from my point of view rubs of nh
referred to dialogue intentions of dialogue act so you something you want to alter relations
and i think you could
have those of the object type definitions if you will to extend to have like
what specific actions you could actual dollars
and
since you're doing however a multimodal and belief tracking or also policy decision you could
if more general we have to this on the on the upper level and then
a more fine grained way of doing this on the lower level which would allow
you to handle these
these things
i would be my files the correctly with the status of errors
it's hard to say i mean i think they are computationally is used to that
it's i think that's just what to basically referring to
and i think you go you can also line i think it depends on the
smart
way of modeling the
you stated so the probability distribution i mean is very simple way of doing the
probability distribution and you want to have
more complex weights maybe you won't have hierarchical one and you have to figure out
how to do these five
it doesn't
but strict the general idea of modeling the dialogue and that way
so
would still allow you to define relations would still allow you to talk about those
relations and i still think is a good idea to have those relations because this
is
from my point of view the only way that the system is also able to
talk about those relations
but where how far this thing going it's hard to say because i don't know
how the new model to look like
no
i mean one thing i would i
the next that i would go to also be that have a probability of what
different types of relation
and also on the entity level which would no longer to have also things like
on one
this all that
i one
not this and how to do these things and i think this is what you
could do if you have like with the distribution operations sort of just the values
that last
but also what the chase them differently
without looking to lead to a given more for even good on something that
okay thank you