elplaguister/DTS_line_datasets · Datasets at Hugging Face

topic_shift bool 2 classes	utterance stringlengths 1 7.9k	session_id stringlengths 7 14
false	Yep.	QMSum_134
false	But I mean , I guess I guess the thing is , uh , this is another , smaller , case of reasoning in the case of an uncertainty , which makes me think Bayes - net should be the way to solve these things. So if you had If for every construction ,	QMSum_134
false	Oh !	QMSum_134
false	right ? you could say , " Well , there Here 's the Where - Is construction. " And for the Where - Is construction , we know we need to l look at this node , that merges these three things together	QMSum_134
false	Mm - hmm.	QMSum_134
false	as for th to decide the response. And since we have a finite number of constructions that we can deal with , we could have a finite number of nodes.	QMSum_134
false	OK. Mm - hmm.	QMSum_134
false	Say , if we had to y deal with arbitrary language , it wouldn't make any sense to do that , because there 'd be no way to generate the nodes for every possible sentence.	QMSum_134
false	Mm - hmm.	QMSum_134
false	But since we can only deal with a finite amount of stuff	QMSum_134
false	So , basically , the idea is to f to feed the output of that belief - net into another belief - net.	QMSum_134
false	Yeah , so basically take these three things and then put them into another belief - net.	QMSum_134
false	But , why why why only those three ? Why not the whol	QMSum_134
false	Well , I mean , d For the Where - Is question. So we 'd have a node for the Where - Is question.	QMSum_134
false	Yeah. But we believe that all the decision nodes are can be relevant for the Where - Is , and the Where How - do - I - get - to or the Tell - me - something - about.	QMSum_134
false	You can come in if you want.	QMSum_134
false	Yes , it is allowed.	QMSum_134
false	As long as y you 're not wearing your h your h headphones. Well , I do I See , I don't know if this is a good idea or not. I 'm just throwing it out. But uh , it seems like we could have I mea or uh we could put all of the all of the r information that could also be relevant into the Where - Is node answer	QMSum_134
false	Mm - hmm. Yep.	QMSum_134
false	node thing stuff. And uh	QMSum_134
false	OK.	QMSum_134
false	I mean Let 's not forget we 're gonna get some very strong input from these sub dis from these discourse things , right ? So. " Tell me the location of X. " Nuh ? Or " Where is X located at ? "	QMSum_134
false	We u	QMSum_134
false	Nuh ?	QMSum_134
false	Yeah , I know , but the Bayes - net would be able to The weights on the on the nodes in the Bayes - net would be able to do all that ,	QMSum_134
true	Mm - hmm.	QMSum_134
false	wouldn't it ? Here 's a k Oh ! Oh , I 'll wait until you 're plugged in. Oh , don't sit there. Sit here. You know how you don't like that one. It 's OK. That 's the weird one. That 's the one that 's painful. That hurts. It hurts so bad. I 'm h I 'm happy that they 're recording that. That headphone. The headphone that you have to put on backwards , with the little little thing and the little little foam block on it ? It 's a painful , painful microphone.	QMSum_134
false	I think it 's th called " the Crown ".	QMSum_134
false	The crown ?	QMSum_134
false	What ?	QMSum_134
false	Yeah , versus " the Sony ".	QMSum_134
false	The Crown ? Is that the actual name ? OK.	QMSum_134
false	Mm - hmm. The manufacturer.	QMSum_134
false	I don't see a manufacturer on it.	QMSum_134
false	You w	QMSum_134
false	Oh , wait , here it is. h This thingy. Yeah , it 's " The Crown ". The crown of pain !	QMSum_134
false	Yes.	QMSum_134
false	You 're on - line ?	QMSum_134
false	Are you are your mike o Is your mike on ?	QMSum_134
false	Indeed.	QMSum_134
false	OK. So you 've been working with these guys ? You know what 's going on ?	QMSum_134
false	Yes , I have. And , I do. Yeah , alright. s So where are we ?	QMSum_134
false	Excellent !	QMSum_134
false	We 're discussing this.	QMSum_134
false	I don't think it can handle French , but anyway.	QMSum_134
false	So. Assume we have something coming in. A person says , " Where is X ? " , and we get a certain We have a Situation vector and a User vector and everything is fine ? An - an and and our and our	QMSum_134
false	Did you just sti Did you just stick the m the the the microphone actually in the tea ?	QMSum_134
false	No.	QMSum_134
false	And , um ,	QMSum_134
false	I 'm not drinking tea. What are you talking about ?	QMSum_134
false	Oh , yeah. Sorry.	QMSum_134
false	let 's just assume our Bayes - net just has three decision nodes for the time being. These three , he wants to know something about it , he wants to know where it is , he wants to go there.	QMSum_134
false	In terms of , these would be wha how we would answer the question Where - Is , right ? We u This is i That 's what you s it seemed like , explained it to me earlier	QMSum_134
false	Yeah , but , mmm.	QMSum_134
false	w We we 're we wanna know how to answer the question " Where is X ? "	QMSum_134
false	Yeah. No , I can I can do the Timing node in here , too , and say " OK. "	QMSum_134
false	Well , yeah , but in the s uh , let 's just deal with the s the simple case of we 're not worrying about timing or anything. We just want to know how we should answer " Where is X ? "	QMSum_134
false	OK. And , um , OK , and , Go - there has two values , right ? , Go - there and not - Go - there. Let 's assume those are the posterior probabilities of that.	QMSum_134
false	Mm - hmm.	QMSum_134
false	Info - on has True or False and Location. So , he wants to know something about it , and he wants to know something he wants to know Where - it - is ,	QMSum_134
false	Excuse me.	QMSum_134
false	has these values. And , um ,	QMSum_134
false	Oh , I see why we can't do that.	QMSum_134
false	And , um , in this case we would probably all agree that he wants to go there. Our belief - net thinks he wants to go there ,	QMSum_134
false	Yeah.	QMSum_134
false	right ?	QMSum_134
false	Mm - hmm.	QMSum_134
false	In the , uh , whatever , if we have something like this here , and this like that and maybe here also some	QMSum_134
false	You should probably make them out of Yeah.	QMSum_134
false	something like that ,	QMSum_134
false	Well , it	QMSum_134
false	then we would guess , " Aha ! He , our belief - net , has s stronger beliefs that he wants to know where it is , than actually wants to go there. " Right ?	QMSum_134
false	That it Doesn't this assume , though , that they 're evenly weighted ?	QMSum_134
false	True.	QMSum_134
false	Like I guess they are evenly weighted.	QMSum_134
false	The different decision nodes , you mean ?	QMSum_134
false	Yeah , the Go - there , the Info - on , and the Location ?	QMSum_134
false	Well , d yeah , this is making the assumption. Yes.	QMSum_134
false	Like	QMSum_134
false	What do you mean by " differently weighted " ? They don't feed into anything really anymore.	QMSum_134
false	But I mean , why do we	QMSum_134
false	Or I jus	QMSum_134
false	If we trusted the Go - there node more th much more than we trusted the other ones , then we would conclude , even in this situation , that he wanted to go there.	QMSum_134
false	Le	QMSum_134
false	So , in that sense , we weight them equally right now.	QMSum_134
false	OK. Makes sense. Yeah. But	QMSum_134
false	So the But I guess the k the question that I was as er wondering or maybe Robert was proposing to me is How do we d make the decision on as to which one to listen to ?	QMSum_134
false	Yeah , so , the final d decision is the combination of these three. So again , it 's it 's some kind of , uh	QMSum_134
false	Bayes - net.	QMSum_134
false	Yeah , sure.	QMSum_134
false	OK so , then , the question i So then my question is t to you then , would be So is the only r reason we can make all these smaller Bayes - nets , because we know we can only deal with a finite set of constructions ? Cuz oth If we 're just taking arbitrary language in , we couldn't have a node for every possible question , you know ?	QMSum_134
false	A decision node for every possible question , you mean ?	QMSum_134
false	Well , I like , in the case of Yeah. In the ca Any piece of language , we wouldn't be able to answer it with this system , b if we just h Cuz we wouldn't have the correct node. Basically , w what you 're s proposing is a n Where - Is node , right ?	QMSum_134
false	Yeah.	QMSum_134
false	And and if we And if someone says , you know , uh , something in Mandarin to the system , we 'd - wouldn't know which node to look at to answer that question ,	QMSum_134
false	So is Yeah. Yeah.	QMSum_134
false	right ?	QMSum_134
false	Mmm ?	QMSum_134
false	So , but but if we have a finite What ?	QMSum_134
false	I don't see your point. What what what I am thinking , or what we 're about to propose here is we 're always gonna get the whole list of values and their posterior probabilities. And now we need an expert system or belief - net or something that interprets that , that looks at all the values and says , " The winner is Timing. Now , go there. " " Uh , go there , Timing , now. " Or , " The winner is Info - on , Function - Off. " So , he wants to know something about it , and what it does. Nuh ? Uh , regardless of of of the input. Wh - Regardle	QMSum_134

false

Yep.

QMSum_134

false

But I mean , I guess I guess the thing is , uh , this is another , smaller , case of reasoning in the case of an uncertainty , which makes me think Bayes - net should be the way to solve these things. So if you had If for every construction ,

QMSum_134

false

Oh !

QMSum_134

false

right ? you could say , " Well , there Here 's the Where - Is construction. " And for the Where - Is construction , we know we need to l look at this node , that merges these three things together

QMSum_134

false

Mm - hmm.

QMSum_134

false

as for th to decide the response. And since we have a finite number of constructions that we can deal with , we could have a finite number of nodes.

QMSum_134

false

OK. Mm - hmm.

QMSum_134

false

Say , if we had to y deal with arbitrary language , it wouldn't make any sense to do that , because there 'd be no way to generate the nodes for every possible sentence.

QMSum_134

false

Mm - hmm.

QMSum_134

false

But since we can only deal with a finite amount of stuff

QMSum_134

false

So , basically , the idea is to f to feed the output of that belief - net into another belief - net.

QMSum_134

false

Yeah , so basically take these three things and then put them into another belief - net.

QMSum_134

false

But , why why why only those three ? Why not the whol

QMSum_134

false

Well , I mean , d For the Where - Is question. So we 'd have a node for the Where - Is question.

QMSum_134

false

Yeah. But we believe that all the decision nodes are can be relevant for the Where - Is , and the Where How - do - I - get - to or the Tell - me - something - about.

QMSum_134

false

You can come in if you want.

QMSum_134

false

Yes , it is allowed.

QMSum_134

false

As long as y you 're not wearing your h your h headphones. Well , I do I See , I don't know if this is a good idea or not. I 'm just throwing it out. But uh , it seems like we could have I mea or uh we could put all of the all of the r information that could also be relevant into the Where - Is node answer

QMSum_134

false

Mm - hmm. Yep.

QMSum_134

false

node thing stuff. And uh

QMSum_134

false

OK.

QMSum_134

false

I mean Let 's not forget we 're gonna get some very strong input from these sub dis from these discourse things , right ? So. " Tell me the location of X. " Nuh ? Or " Where is X located at ? "

QMSum_134

false

We u

QMSum_134

false

Nuh ?

QMSum_134

false

Yeah , I know , but the Bayes - net would be able to The weights on the on the nodes in the Bayes - net would be able to do all that ,

QMSum_134

true

Mm - hmm.

QMSum_134

false

wouldn't it ? Here 's a k Oh ! Oh , I 'll wait until you 're plugged in. Oh , don't sit there. Sit here. You know how you don't like that one. It 's OK. That 's the weird one. That 's the one that 's painful. That hurts. It hurts so bad. I 'm h I 'm happy that they 're recording that. That headphone. The headphone that you have to put on backwards , with the little little thing and the little little foam block on it ? It 's a painful , painful microphone.

QMSum_134

false

I think it 's th called " the Crown ".

QMSum_134

false

The crown ?

QMSum_134

false

What ?

QMSum_134

false

Yeah , versus " the Sony ".

QMSum_134

false

The Crown ? Is that the actual name ? OK.

QMSum_134

false

Mm - hmm. The manufacturer.

QMSum_134

false

I don't see a manufacturer on it.

QMSum_134

false

You w

QMSum_134

false

Oh , wait , here it is. h This thingy. Yeah , it 's " The Crown ". The crown of pain !

QMSum_134

false

Yes.

QMSum_134

false

You 're on - line ?

QMSum_134

false

Are you are your mike o Is your mike on ?

QMSum_134

false

Indeed.

QMSum_134

false

OK. So you 've been working with these guys ? You know what 's going on ?

QMSum_134

false

Yes , I have. And , I do. Yeah , alright. s So where are we ?

QMSum_134

false

Excellent !

QMSum_134

false

We 're discussing this.

QMSum_134

false

I don't think it can handle French , but anyway.

QMSum_134

false

So. Assume we have something coming in. A person says , " Where is X ? " , and we get a certain We have a Situation vector and a User vector and everything is fine ? An - an and and our and our

QMSum_134

false

Did you just sti Did you just stick the m the the the microphone actually in the tea ?

QMSum_134

false

No.

QMSum_134

false

And , um ,

QMSum_134

false

I 'm not drinking tea. What are you talking about ?

QMSum_134

false

Oh , yeah. Sorry.

QMSum_134

false

let 's just assume our Bayes - net just has three decision nodes for the time being. These three , he wants to know something about it , he wants to know where it is , he wants to go there.

QMSum_134

false

In terms of , these would be wha how we would answer the question Where - Is , right ? We u This is i That 's what you s it seemed like , explained it to me earlier

QMSum_134

false

Yeah , but , mmm.

QMSum_134

false

w We we 're we wanna know how to answer the question " Where is X ? "

QMSum_134

false

Yeah. No , I can I can do the Timing node in here , too , and say " OK. "

QMSum_134

false

Well , yeah , but in the s uh , let 's just deal with the s the simple case of we 're not worrying about timing or anything. We just want to know how we should answer " Where is X ? "

QMSum_134

false

OK. And , um , OK , and , Go - there has two values , right ? , Go - there and not - Go - there. Let 's assume those are the posterior probabilities of that.

QMSum_134

false

Mm - hmm.

QMSum_134

false

Info - on has True or False and Location. So , he wants to know something about it , and he wants to know something he wants to know Where - it - is ,

QMSum_134

false

Excuse me.

QMSum_134

false

has these values. And , um ,

QMSum_134

false

Oh , I see why we can't do that.

QMSum_134

false

And , um , in this case we would probably all agree that he wants to go there. Our belief - net thinks he wants to go there ,

QMSum_134

false

Yeah.

QMSum_134

false

right ?

QMSum_134

false

Mm - hmm.

QMSum_134

false

In the , uh , whatever , if we have something like this here , and this like that and maybe here also some

QMSum_134

false

You should probably make them out of Yeah.

QMSum_134

false

something like that ,

QMSum_134

false

Well , it

QMSum_134

false

then we would guess , " Aha ! He , our belief - net , has s stronger beliefs that he wants to know where it is , than actually wants to go there. " Right ?

QMSum_134

false

That it Doesn't this assume , though , that they 're evenly weighted ?

QMSum_134

false

True.

QMSum_134

false

Like I guess they are evenly weighted.

QMSum_134

false

The different decision nodes , you mean ?

QMSum_134

false

Yeah , the Go - there , the Info - on , and the Location ?

QMSum_134

false

Well , d yeah , this is making the assumption. Yes.

QMSum_134

false

Like

QMSum_134

false

What do you mean by " differently weighted " ? They don't feed into anything really anymore.

QMSum_134

false

But I mean , why do we

QMSum_134

false

Or I jus

QMSum_134

false

If we trusted the Go - there node more th much more than we trusted the other ones , then we would conclude , even in this situation , that he wanted to go there.

QMSum_134

false

Le

QMSum_134

false

So , in that sense , we weight them equally right now.

QMSum_134

false

OK. Makes sense. Yeah. But

QMSum_134

false

So the But I guess the k the question that I was as er wondering or maybe Robert was proposing to me is How do we d make the decision on as to which one to listen to ?

QMSum_134

false

Yeah , so , the final d decision is the combination of these three. So again , it 's it 's some kind of , uh

QMSum_134

false

Bayes - net.

QMSum_134

false

Yeah , sure.

QMSum_134

false

OK so , then , the question i So then my question is t to you then , would be So is the only r reason we can make all these smaller Bayes - nets , because we know we can only deal with a finite set of constructions ? Cuz oth If we 're just taking arbitrary language in , we couldn't have a node for every possible question , you know ?

QMSum_134

false

A decision node for every possible question , you mean ?

QMSum_134

false

Well , I like , in the case of Yeah. In the ca Any piece of language , we wouldn't be able to answer it with this system , b if we just h Cuz we wouldn't have the correct node. Basically , w what you 're s proposing is a n Where - Is node , right ?

QMSum_134

false

Yeah.

QMSum_134

false

And and if we And if someone says , you know , uh , something in Mandarin to the system , we 'd - wouldn't know which node to look at to answer that question ,

QMSum_134

false

So is Yeah. Yeah.

QMSum_134

false

right ?

QMSum_134

false

Mmm ?

QMSum_134

false

So , but but if we have a finite What ?

QMSum_134

false

I don't see your point. What what what I am thinking , or what we 're about to propose here is we 're always gonna get the whole list of values and their posterior probabilities. And now we need an expert system or belief - net or something that interprets that , that looks at all the values and says , " The winner is Timing. Now , go there. " " Uh , go there , Timing , now. " Or , " The winner is Info - on , Function - Off. " So , he wants to know something about it , and what it does. Nuh ? Uh , regardless of of of the input. Wh - Regardle

QMSum_134