in a few me all

there's or a nineteen was for channel i don't necessity but i am i paper

is "'cause" if a chance for adaptive language understanding

this is also my whole

firstly i will give some

with its knowledge about school language understanding

this is the time of spoken dialogue system slu module serves as the interface between

asr

and then

and it reminded management motive

the input of all slu it's word sequence and also is all what is meant

for example the user's is only flies from both the new york

and all for all slu can be

in these to find a flies and the city also

that's it you all you partial is both a and basically also destination is we

off and let the m can make some of these issues about how to give

a good we apply for you

do you recall rate slu can be viewed as a sequence labeling problem that is

included can be what a sequence and the output is a slot sequence

is a example

yes a example the i own representation is used the force higher than that no

it means no slots for a carnival it and eating and i

is a on use the two tasks and you was for long as well

and the and finally we can get us some slot value yes

if we have sufficient in-domain they how with

a human okay she we use easy to us test actually slu a system with

a deep learning models now

you know left part of his

yes em or do for all

one time and the right about everything and then for learning curve all yes em

all at stance that suppose that the performance of the ask him a low heavily

relies on how much data we used for training

us all other all-pole of all probability is that we have no sufficient intimately always

is visual when we need a new domain so a data collection and annotation is

is very it can also

very expensive and time-consuming

so we have to space

then you might result

a small and all the news articles that may or even a totally new dialogue

i will show some examples about the new ideas and use lost

in eliciting to some hosting to train changes it

that's let's say francisco is the city name of stroll okay she

well as the

well with a disgusted to name of a tool okay she and can't afford infinite

maiden name also location in a few times as the data name

location

so i think first and those of the test set all still should

is a relatively new while you to solve a slot or location or the policy

to you "'cause" a low is seen in the training change in their bodies

is not a common to all not at the by from location compensated is doesn't

and viewed as a difference well so expensive and you want but at the seven

isn't as we can find that

i and number

absolute new binding useful

for

probably to some people to layout and mse training data

next we can also classify and a new slots into two

i into a retinue well and absolutely wall

here is for example it does not stop or location or probably can be a

can

can the outcome competition was also you can you can see is the sloth

well applies at least one is that so value one so

so in you all paper we want to tackle the for a relatively new values

and relatively new slots in a conversation with

here we propose one possible way to us also propose all

a relatively new one minus lost

is at one because that we manually speech every slot into a small hands

for at home because that

each other at some concepts

right exactly list in a unified the only one

and a lot of these slots distributed

then as a whole of at all because that

it is for many work for example let's talk about the city of impartial

may have different ways

like

firstly the city name of are located on the city name false at the of

the partial

the speeding that's lost entomology actions we get only one

replacing

one trouble for okay sentence

so the of the partial

last

and then

procedure of using a protocols that is all depends mainly work

do you see is that spatial way case

into now is just one can be represented as a couple of atomic on that

they are some examples of the stuff i don't know about some slots and representations

based on the concept

let's see the user interface also i from because at first but also or a

lot older colours that have a however you are was a relative shaded area

this is an example we have used in the previous slice

if we want to a predicate predict the label also

sometimes

but also is that everything value for that slot for location policy

and the second the contest going to leave is also on the full

but if we model the slots this all at all because that's

we can find that of course there is actually see for

sitting and in the context i'm going to leave

is the or from a location in the chain so let's by the

at one constantly help you

and an overwhelming

we haven't a pretty some new slot at a time

compensation

g is an example if we have only a list two slots a location don't

city name and it worked in boston

maybe also found

the new slot from location tones the name and location on state

g it lists like this about how to morally so that it is not a

because that's

in the traditional model with a rifle because that we have only one class classifier

prediction that's lost

but if we represent

just wanna buy at all

yes or no because that

but strong can be i'll we present

so that i

as a

it impossible for example here of cells at it is defined as a couple of

state and for location

so we propose to simple yes no too much time based on

at home concept

the first method are just there is simply a considers the different part of f

and tahoe as

independent of classification task

g r and of from

yell of data name and from okay she is predicted independent and the by the

way in the i was he might also predicted by and are

another classifier

in a similar mass of the weights useful work considers and a different part of

the at how as

a parallel task

is it you can sample anyway

a lot of trouble pretty if you also location depends on all to

output of fifteen

elliptical least stage

no

the prediction can be can't you declare their collect all atoms in the top or

back here the predicted

and this is a

now what's wrong is represented by a couple of i still

yugoslav maybe produce which is a levels on choice

but we all okay but we didn't

we just should or shouldn't nice

goal slot s and one prediction without any position

a formal nasa a so that only concept

it has been realized khomeini walk

a nice

in this in the nist lots of human knowledge and it may is not so

is there any easy way we want to ask

we define a light

no policy was also or name can be on a sure way for speed and

a slot into single path

right well known and obtain a sequence of surrounding it is very easy and whatnot

well i know what a simple this is not real structure as in the top

of atomic also so

so we propose you will see i think of the model to encode a slot

name into a back to wait for it what no slot surrounding in it is

that can also distribute distributed representation for a small

and first of all we need to make any assumption that is just a name

is that meaning for natural language description

so we will for instance

and last fall subordinating both e

a in this work i didn't ask personally way to have a

a slot encoder is also a yes tomorrow which k

whose input is sorely that we i

no to find a final to the wireless of both i wanna fast and the

forward pass

a concatenating can see that would be a strong looking at

no for distorted we have i story many we drive to our work was actually

at each subordinating and

and there's utilize at a car in the time step

no we can get a scroll it with the same size as data

a smaller number all

and also we k and a softmax normalization is us go back there

no let's go to la experiments we evaluate our method on two task

by the set of mismatch and domain adaptation

the first task but a set of mismatch this all ages

which is widely used it as a benchmark e slu community

it has about five so the centres for change and

my hundred sentences for task

and it is lost this including and every slot is represented by a hubble happened

with that is to the first time energy contours

a first time is introduced for forty five at from the concept and the set

of that is inconsistent atomic

to bidirectional a generalisation july difficult to do for relative regimen use lost

we you and you

task that h is x test which is a mismatch with the changes that you

are mostly want to use some cases about relative a new value

channels

for example

the city name is called a by follow cg and acoustic of the difference in

training in training data

the city name only covered by from c t is relatively new to the slot

to see so we

we just randomly in replacing the while you all to say the in the

it is passed that

without relatively new well known we can data it is x test sample

no

as a challenging

is a experimental results of all the mass at all

it just ages and ages extract

first we can see last let h is x test is really you can challenging

for the traditional so on

so i'll

time model

the performance drops from a ninety five for someone not

and as a as a single best we also add a recognition already feature at

that

additional input for that yes

and it improves

improves

a slightly about one

persons on

or at x k

and bimodal by morally atomic let slot by

at home because that we can find that

let independent model

a unified implement bounded dependent model yet i can actually a you can increase of

the eighties and

and also

it again also case that significant improvement

or a standard at saps

over the original

yes model

as we said that at all because that uses a lot of human operation and

it may be designed so to overcome this weakness and the slot invading us things

be performance

very pointless

us we can find that is a little bit in

you domain and independent

and if we use

you e

you where use i preach everybody may need for initialization we can find that if

they implement it is much problem into a dependable

so fun we'll we also want to a have a look at a low published

result homepages x

in the centre eighties

we have a lightning in if where using a single so that i mean past

our already got with its the past promenade a sickening

we also performed for is german only

but maybe

adaptation to better but it but it in the mocap data for the

you

rate from a new slot

no yes it will use of multiple supply which causes about two thousand dialogues and

yes

is used

i think the target domain but we have you know analysis for

only adaptation

duration was way have several unused possibility is to switch

well the result shows that the training data also must also meant yes two k

helpful an slu model in the target domain

because

nell some slots you can taste initials less all and talking and comments

and it you will model the slot value of s o from concept

the improvement okay a we can get a for it

finally i will give some

two examples

two cases to for discussion that in the left

in left side this about

the

so what confirmed or has infinite learned the svm guitar tone prediction because the requirement

that have something is only for slot

come from

or has tv in the see that it

but the

all more okay

and the actual concept here we use these global or current sample has internet

and read a similar case will rise it is up on a new slot component

or to be a long before never if it is in the selected

after that at times of come from and it should be allowed it is so

all master s or for me

a total conversation okay

can find and yourself

if not i can give some conclusions

and first well defined at all because that's

can have activated at data sparsity problem of slu and method of selecting many which

can be extracted and then

automatic or least in there is very promising it is usual what we also want

to explore popular at some because m with yes

is the whole encourage

maybe we can use some a cross language

what do i

i did try

it was

this example shows two

to slot from cd electricity labels have a part of the city

but in the city name combine these two slots

after different

the and not on the say

so you know training there are low

city names we covered by from c d is housing to reconsider

so if we attack

so the task and if way

you some relatively new

but it was for to set in the test set

if you can be a challenging

for that

to proceed

this model

i see it is very easy because

this design can what my figure

in ages because this we designed a buyer that data from provided

but only for some for something to say that we use we use although for

this paper we use some try

chinese data set we must to

do you do that at one because they have to follow based

for a screen

for each plane labels