elplaguister/DTS_line_datasets · Datasets at Hugging Face

topic_shift bool 2 classes	utterance stringlengths 1 7.9k	session_id stringlengths 7 14
false	Yeah.	QMSum_120
false	And they they did a lot of experiments where th where , um , they take speech and , um , e they modify the , uh they they they measure the relative importance of having different , um , portions of the modulation spectrum intact.	QMSum_120
false	Yeah.	QMSum_120
false	And they find that the the spectrum between one and sixteen hertz in the modulation is , uh is im important for speech recognition.	QMSum_120
false	Sure. I mean , this sort of goes back to earlier stuff by Drullman.	QMSum_120
false	Um.	QMSum_120
false	And and , uh , the the MSG features were sort of built up with this notion	QMSum_120
false	Yeah. Right.	QMSum_120
false	But , I guess , I thought you had brought this up in the context of , um , targets somehow.	QMSum_120
false	Right.	QMSum_120
false	But i m	QMSum_120
false	Um	QMSum_120
false	i it 's not I mean , they 're sort of not in the same kind of category as , say , a phonetic target or a syllabic target	QMSum_120
false	Mmm. Mm - hmm.	QMSum_120
false	or a	QMSum_120
false	Um , I was thinking more like using them as as the inputs to to the detectors.	QMSum_120
false	or a feature or something. Oh , I see. Well , that 's sort of what MSG does.	QMSum_120
false	Yeah. Yeah.	QMSum_120
false	Right ? So it 's	QMSum_120
false	Mm - hmm.	QMSum_120
false	But but , uh	QMSum_120
false	Yeah.	QMSum_120
false	Yeah.	QMSum_120
false	Anyway , we 'll talk more about it later.	QMSum_120
false	So I guess this is more or less now just to get you up to date , Johno. This is what , uh ,	QMSum_134
false	This is a meeting for me.	QMSum_134
false	um , Eva , Bhaskara , and I did.	QMSum_134
false	Did you add more stuff to it ? later ?	QMSum_134
false	Um. Why ?	QMSum_134
false	Um. I don't know. There were , like , the you know , @ @ and all that stuff. But. I thought you you said you were adding stuff	QMSum_134
false	Uh , no.	QMSum_134
false	but I don't know.	QMSum_134
false	This is Um , Ha ! Very nice. Um , so we thought that , We can write up uh , an element , and for each of the situation nodes that we observed in the Bayes - net ? So. What 's the situation like at the entity that is mentioned ? if we know anything about it ? Is it under construction ? Or is it on fire or something happening to it ? Or is it stable ? and so forth , going all the way um , f through Parking , Location , Hotel , Car , Restroom , @ @ Riots , Fairs , Strikes , or Disasters.	QMSum_134
false	So is This is A situation are is all the things which can be happening right now ? Or , what is the situation type ?	QMSum_134
false	That 's basically just specifying the the input for the w what 's	QMSum_134
false	Oh , I see y Why are you specifying it in XML ?	QMSum_134
false	Um. Just because it forces us to be specific about the values here ?	QMSum_134
false	OK.	QMSum_134
false	And , also , I mean , this is a what the input is going to be. Right ? So , we will , uh This is a schema. This is	QMSum_134
false	Well , yeah. I just don't know if this is th l what the Does This is what Java Bayes takes ? as a Bayes - net spec ?	QMSum_134
false	No , because I mean if we I mean we 're sure gonna interface to We 're gonna get an XML document from somewhere. Right ? And that XML document will say " We are able to We were able to observe that w the element , um , @ @ of the Location that the car is near. " So that 's gonna be Um.	QMSum_134
false	So this is the situational context , everything in it. Is that what Situation is short for , shi situational context ?	QMSum_134
false	Yep.	QMSum_134
false	OK.	QMSum_134
false	So this is just , again , a an XML schemata which defines a set of possible , uh , permissible XML structures , which we view as input into the Bayes - net. Right ?	QMSum_134
false	And then we can r uh possibly run one of them uh transformations ? That put it into the format that the Bayes n or Java Bayes or whatever wants ?	QMSum_134
false	Yea - Are you talking are you talking about the the structure ?	QMSum_134
false	Well it	QMSum_134
false	I mean when you observe a node.	QMSum_134
false	When you when you say the input to the v Java Bayes , it takes a certain format ,	QMSum_134
false	Um - hmm.	QMSum_134
false	right ? Which I don't think is this. Although I don't know.	QMSum_134
false	No , it 's certainly not this. Nuh.	QMSum_134
false	So you could just Couldn't you just run a	QMSum_134
false	XSL. Yeah.	QMSum_134
false	Yeah. To convert it into the Java Bayes for format ?	QMSum_134
false	Yep.	QMSum_134
false	OK.	QMSum_134
false	That 's That 's no problem , but I even think that , um I mean , once Once you have this sort of as running as a module Right ? What you want is You wanna say , " OK , give me the posterior probabilities of the Go - there node , when this is happening. " Right ? When the person said this , the car is there , it 's raining , and this is happening. And with this you can specify the what 's happening in the situation , and what 's happening with the user. So we get After we are done , through the Situation we get the User Vector. So , this is a	QMSum_134
false	So this is just a specification of all the possible inputs ?	QMSum_134
false	Yep. And , all the possible outputs , too. So , we have , um , for example , the , uh , Go - there decision node	QMSum_134
false	OK.	QMSum_134
false	which has two elements , going - there and its posterior probability , and not - going - there and its posterior probability , because the output is always gonna be all the decision nodes and all the the a all the posterior probabilities for all the values.	QMSum_134
false	And then we would just look at the , eh , Struct that we wanna look at in terms of if if we 're only asking about one of the So like , if I 'm just interested in the going - there node , I would just pull that information out of the Struct that gets return that would that Java Bayes would output ?	QMSum_134
false	Um , pretty much , yes , but I think it 's a little bit more complex. As , if I understand it correctly , it always gives you all the posterior probabilities for all the values of all decision nodes. So , when we input something , we always get the , uh , posterior probabilities for all of these. Right ?	QMSum_134
false	OK.	QMSum_134
false	So there is no way of telling it t not to tell us about the EVA values.	QMSum_134
false	Yeah , wait I agree , that 's yeah , use oh , uh Yeah , OK.	QMSum_134
false	So so we get this whole list of of , um , things , and the question is what to do with it , what to hand on , how to interpret it , in a sense. So y you said if you " I 'm only interested in whether he wants to go there or not " , then I just look at that node , look which one	QMSum_134
false	Look at that Struct in the output ,	QMSum_134
false	Yep.	QMSum_134
false	right ?	QMSum_134
false	Look at that Struct in the the output , even though I wouldn't call it a " Struct ". But.	QMSum_134
false	Well i well , it 's an XML Structure that 's being res returned ,	QMSum_134
false	Oh. Mm - hmm.	QMSum_134
false	right ?	QMSum_134
false	So every part of a structure is a " Struct ". Yeah.	QMSum_134
false	Yeah , I just uh I just was abbreviated it to Struct in my head , and started going with that.	QMSum_134
false	That element or object , I would say.	QMSum_134
false	Not a C Struct. That 's not what I was trying to k	QMSum_134
false	Yeah.	QMSum_134
false	though yeah.	QMSum_134
false	OK. And , um , the reason is why I think it 's a little bit more complex or why why we can even think about it as an interesting problem in and of itself is Um. So. The , uh Let 's look at an example.	QMSum_134
false	Well , w wouldn't we just take the structure that 's outputted and then run another transformation on it , that would just dump the one that we wanted out ?	QMSum_134
false	Yeah. w We 'd need to prune. Right ? Throw things away.	QMSum_134
false	Well , actually , you don't even need to do that with XML.	QMSum_134
false	No	QMSum_134
false	D Can't you just look at one specific	QMSum_134
false	Yeah , exactly. The @ @ Xerxes allows you to say , u " Just give me the value of that , and that , and that. " But , we don't really know what we 're interested in before we look at the complete at at the overall result. So the person said , um , " Where is X ? " and so , we want to know , um , is Does he want info ? o on this ? or know the location ? Or does he want to go there ? Let 's assume this is our our question.	QMSum_134
false	Sure.	QMSum_134
false	Nuh ? So. Um. Do this in Perl. So we get OK. Let 's assume this is the output. So. We should con be able to conclude from that that I mean. It 's always gonna give us a value of how likely we think i it is that he wants to go there and doesn't want to go there , or how likely it is that he wants to get information. But , maybe w we should just reverse this to make it a little bit more delicate. So , does he wanna know where it is ? or does he wanna go there ?	QMSum_134
false	He wants to know where it is.	QMSum_134
false	Right. I I I tend to agree. And if it 's If	QMSum_134
false	Well now , y I mean , you could	QMSum_134
false	And i if there 's sort of a clear winner here , and , um and this is pretty , uh indifferent , then we then we might conclude that he actually wants to just know where , uh t uh , he does want to go there.	QMSum_134
false	Uh , out of curiosity , is there a reason why we wouldn't combine these three nodes ? into one smaller subnet ? that would just basically be the question for We have " where is X ? " is the question , right ? That would just be Info - on or Location ? Based upon	QMSum_134
false	Or Go - there. A lot of people ask that , if they actually just wanna go there. People come up to you on campus and say , " Where 's the library ? " You 're gonna say y you 're gonna say , g " Go down that way. " You 're not gonna say " It 's It 's five hundred yards away from you " or " It 's north of you " , or " it 's located "	QMSum_134
false	Well , I mean But the there 's So you just have three decisions for the final node , that would link thes these three nodes in the net together.	QMSum_134
false	Um. I don't know whether I understand what you mean. But. Again , in this Given this input , we , also in some situations , may wanna postulate an opinion whether that person wants to go there now the nicest way , use a cab , or so s wants to know it wants to know where it is because he wants something fixed there , because he wants to visit t it or whatever. So , it n I mean a All I 'm saying is , whatever our input is , we 're always gonna get the full output. And some some things will always be sort of too not significant enough.	QMSum_134
false	Wha Or i or i it 'll be tight. You won't it 'll be hard to decide.	QMSum_134

false

Yeah.

QMSum_120

false

And they they did a lot of experiments where th where , um , they take speech and , um , e they modify the , uh they they they measure the relative importance of having different , um , portions of the modulation spectrum intact.

QMSum_120

false

Yeah.

QMSum_120

false

And they find that the the spectrum between one and sixteen hertz in the modulation is , uh is im important for speech recognition.

QMSum_120

false

Sure. I mean , this sort of goes back to earlier stuff by Drullman.

QMSum_120

false

Um.

QMSum_120

false

And and , uh , the the MSG features were sort of built up with this notion

QMSum_120

false

Yeah. Right.

QMSum_120

false

But , I guess , I thought you had brought this up in the context of , um , targets somehow.

QMSum_120

false

Right.

QMSum_120

false

But i m

QMSum_120

false

Um

QMSum_120

false

i it 's not I mean , they 're sort of not in the same kind of category as , say , a phonetic target or a syllabic target

QMSum_120

false

Mmm. Mm - hmm.

QMSum_120

false

or a

QMSum_120

false

Um , I was thinking more like using them as as the inputs to to the detectors.

QMSum_120

false

or a feature or something. Oh , I see. Well , that 's sort of what MSG does.

QMSum_120

false

Yeah. Yeah.

QMSum_120

false

Right ? So it 's

QMSum_120

false

Mm - hmm.

QMSum_120

false

But but , uh

QMSum_120

false

Yeah.

QMSum_120

false

Yeah.

QMSum_120

false

Anyway , we 'll talk more about it later.

QMSum_120

false

So I guess this is more or less now just to get you up to date , Johno. This is what , uh ,

QMSum_134

false

This is a meeting for me.

QMSum_134

false

um , Eva , Bhaskara , and I did.

QMSum_134

false

Did you add more stuff to it ? later ?

QMSum_134

false

Um. Why ?

QMSum_134

false

Um. I don't know. There were , like , the you know , @ @ and all that stuff. But. I thought you you said you were adding stuff

QMSum_134

false

Uh , no.

QMSum_134

false

but I don't know.

QMSum_134

false

This is Um , Ha ! Very nice. Um , so we thought that , We can write up uh , an element , and for each of the situation nodes that we observed in the Bayes - net ? So. What 's the situation like at the entity that is mentioned ? if we know anything about it ? Is it under construction ? Or is it on fire or something happening to it ? Or is it stable ? and so forth , going all the way um , f through Parking , Location , Hotel , Car , Restroom , @ @ Riots , Fairs , Strikes , or Disasters.

QMSum_134

false

So is This is A situation are is all the things which can be happening right now ? Or , what is the situation type ?

QMSum_134

false

That 's basically just specifying the the input for the w what 's

QMSum_134

false

Oh , I see y Why are you specifying it in XML ?

QMSum_134

false

Um. Just because it forces us to be specific about the values here ?

QMSum_134

false

OK.

QMSum_134

false

And , also , I mean , this is a what the input is going to be. Right ? So , we will , uh This is a schema. This is

QMSum_134

false

Well , yeah. I just don't know if this is th l what the Does This is what Java Bayes takes ? as a Bayes - net spec ?

QMSum_134

false

No , because I mean if we I mean we 're sure gonna interface to We 're gonna get an XML document from somewhere. Right ? And that XML document will say " We are able to We were able to observe that w the element , um , @ @ of the Location that the car is near. " So that 's gonna be Um.

QMSum_134

false

So this is the situational context , everything in it. Is that what Situation is short for , shi situational context ?

QMSum_134

false

Yep.

QMSum_134

false

OK.

QMSum_134

false

So this is just , again , a an XML schemata which defines a set of possible , uh , permissible XML structures , which we view as input into the Bayes - net. Right ?

QMSum_134

false

And then we can r uh possibly run one of them uh transformations ? That put it into the format that the Bayes n or Java Bayes or whatever wants ?

QMSum_134

false

Yea - Are you talking are you talking about the the structure ?

QMSum_134

false

Well it

QMSum_134

false

I mean when you observe a node.

QMSum_134

false

When you when you say the input to the v Java Bayes , it takes a certain format ,

QMSum_134

false

Um - hmm.

QMSum_134

false

right ? Which I don't think is this. Although I don't know.

QMSum_134

false

No , it 's certainly not this. Nuh.

QMSum_134

false

So you could just Couldn't you just run a

QMSum_134

false

XSL. Yeah.

QMSum_134

false

Yeah. To convert it into the Java Bayes for format ?

QMSum_134

false

Yep.

QMSum_134

false

OK.

QMSum_134

false

That 's That 's no problem , but I even think that , um I mean , once Once you have this sort of as running as a module Right ? What you want is You wanna say , " OK , give me the posterior probabilities of the Go - there node , when this is happening. " Right ? When the person said this , the car is there , it 's raining , and this is happening. And with this you can specify the what 's happening in the situation , and what 's happening with the user. So we get After we are done , through the Situation we get the User Vector. So , this is a

QMSum_134

false

So this is just a specification of all the possible inputs ?

QMSum_134

false

Yep. And , all the possible outputs , too. So , we have , um , for example , the , uh , Go - there decision node

QMSum_134

false

OK.

QMSum_134

false

which has two elements , going - there and its posterior probability , and not - going - there and its posterior probability , because the output is always gonna be all the decision nodes and all the the a all the posterior probabilities for all the values.

QMSum_134

false

And then we would just look at the , eh , Struct that we wanna look at in terms of if if we 're only asking about one of the So like , if I 'm just interested in the going - there node , I would just pull that information out of the Struct that gets return that would that Java Bayes would output ?

QMSum_134

false

Um , pretty much , yes , but I think it 's a little bit more complex. As , if I understand it correctly , it always gives you all the posterior probabilities for all the values of all decision nodes. So , when we input something , we always get the , uh , posterior probabilities for all of these. Right ?

QMSum_134

false

OK.

QMSum_134

false

So there is no way of telling it t not to tell us about the EVA values.

QMSum_134

false

Yeah , wait I agree , that 's yeah , use oh , uh Yeah , OK.

QMSum_134

false

So so we get this whole list of of , um , things , and the question is what to do with it , what to hand on , how to interpret it , in a sense. So y you said if you " I 'm only interested in whether he wants to go there or not " , then I just look at that node , look which one

QMSum_134

false

Look at that Struct in the output ,

QMSum_134

false

Yep.

QMSum_134

false

right ?

QMSum_134

false

Look at that Struct in the the output , even though I wouldn't call it a " Struct ". But.

QMSum_134

false

Well i well , it 's an XML Structure that 's being res returned ,

QMSum_134

false

Oh. Mm - hmm.

QMSum_134

false

right ?

QMSum_134

false

So every part of a structure is a " Struct ". Yeah.

QMSum_134

false

Yeah , I just uh I just was abbreviated it to Struct in my head , and started going with that.

QMSum_134

false

That element or object , I would say.

QMSum_134

false

Not a C Struct. That 's not what I was trying to k

QMSum_134

false

Yeah.

QMSum_134

false

though yeah.

QMSum_134

false

OK. And , um , the reason is why I think it 's a little bit more complex or why why we can even think about it as an interesting problem in and of itself is Um. So. The , uh Let 's look at an example.

QMSum_134

false

Well , w wouldn't we just take the structure that 's outputted and then run another transformation on it , that would just dump the one that we wanted out ?

QMSum_134

false

Yeah. w We 'd need to prune. Right ? Throw things away.

QMSum_134

false

Well , actually , you don't even need to do that with XML.

QMSum_134

false

No

QMSum_134

false

D Can't you just look at one specific

QMSum_134

false

Yeah , exactly. The @ @ Xerxes allows you to say , u " Just give me the value of that , and that , and that. " But , we don't really know what we 're interested in before we look at the complete at at the overall result. So the person said , um , " Where is X ? " and so , we want to know , um , is Does he want info ? o on this ? or know the location ? Or does he want to go there ? Let 's assume this is our our question.

QMSum_134

false

Sure.

QMSum_134

false

Nuh ? So. Um. Do this in Perl. So we get OK. Let 's assume this is the output. So. We should con be able to conclude from that that I mean. It 's always gonna give us a value of how likely we think i it is that he wants to go there and doesn't want to go there , or how likely it is that he wants to get information. But , maybe w we should just reverse this to make it a little bit more delicate. So , does he wanna know where it is ? or does he wanna go there ?

QMSum_134

false

He wants to know where it is.

QMSum_134

false

Right. I I I tend to agree. And if it 's If

QMSum_134

false

Well now , y I mean , you could

QMSum_134

false

And i if there 's sort of a clear winner here , and , um and this is pretty , uh indifferent , then we then we might conclude that he actually wants to just know where , uh t uh , he does want to go there.

QMSum_134

false

Uh , out of curiosity , is there a reason why we wouldn't combine these three nodes ? into one smaller subnet ? that would just basically be the question for We have " where is X ? " is the question , right ? That would just be Info - on or Location ? Based upon

QMSum_134

false

Or Go - there. A lot of people ask that , if they actually just wanna go there. People come up to you on campus and say , " Where 's the library ? " You 're gonna say y you 're gonna say , g " Go down that way. " You 're not gonna say " It 's It 's five hundred yards away from you " or " It 's north of you " , or " it 's located "

QMSum_134

false

Well , I mean But the there 's So you just have three decisions for the final node , that would link thes these three nodes in the net together.

QMSum_134

false

Um. I don't know whether I understand what you mean. But. Again , in this Given this input , we , also in some situations , may wanna postulate an opinion whether that person wants to go there now the nicest way , use a cab , or so s wants to know it wants to know where it is because he wants something fixed there , because he wants to visit t it or whatever. So , it n I mean a All I 'm saying is , whatever our input is , we 're always gonna get the full output. And some some things will always be sort of too not significant enough.

QMSum_134

false

Wha Or i or i it 'll be tight. You won't it 'll be hard to decide.

QMSum_134