hi everybody and then attention from cmu this the joint work done with my colleagues

and one vice the mixing and so they might talk is going to talk about

how to making a charity slot filling systems using the encoder decoder models

so the slot filling system is the focus of this talk and it has been

a device critically in recent years because we have better models more data

but the is the one limitation of a just a two-pass approach is most of

the proposed approach is sometimes of dialogue system is the with domain specific and it

is restricted to one type of dialogue act states and there is not very domain-dependent

so because of that we have difficulty of extending existing operating system

when you don't lengths on your skills

and a second difficulties sometime in a way deploy a system in real life this

always out-of-domain a request and it's gonna be very limited to handling those out-of-domain request

so our goal is we want to develop a domain general system that is not

restricted to a the define predefine acts of state even this task

and the encoder-decoder model things a promising a choice because it's a very powerful and

its prosody model and it has been successfully applied to translation and the open domain

training

so the idea is quite simple we have a encoder that encodes the dialogue history

and of generating a dialog state representation and we have a decoder that you called

the system responds spoken by token

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