D: Mmm .
D: Yeah . Whatever .
A: No way .
E: Way !
D: Is it ?
A: Someone 's gonna start making Phil Collins jokes .
D: Yeah .
D: Hmm ?
A: Sorry .
E: Oh , god , I hope not .
B: What ?
A: You guys are too young .
E: You know like " Sussudio " ,
E: that horrible , horrible song that should never have been created .
A: Yeah , come on .
B: Oh , oh , oh , oh .
A: I know , that was horrible .
B: I 've blocked every aspect of Phil Collins out of my mind .
A: Sussudio .
C: What ?
A: I 'm sorry , I haven't .
A: Not on purpose .
E: in here
D: Oh  Well , also he 's talking about suicide ,
D: and that 's that 's not a notion I wanna have evoked .
A: No , he 's not .
D: He is .
A: Really ?
A: Oops . <comment> I didn't really listen to it ,
D: The A: I was too young .
E: Hmm .
E: It sounds too rocking for that .
A: Anyway .
A: Yeah .
E: Anyway . So , what 's going on here ?
E: So what are what E: Was wollte der Kuenstler uns damit sagen ?
D: So ,
A: Stop excluding me .
D: OK , so we have tons of little things here ,
D: and we 've
A: I can't believe that that 's never been thought of before .
B: Wait , what are the dots ?
B: I don't remember what the dots were .
E: Those are little bugs .
D: OK .
A: Cool Keith .
D: You know , these are our , whatever , belief - net decision nodes ,
D: and they all contribute to these <nonvocalsound> things down here .
B: Oh , oh .
A: Wait , wait , what 's the middle thing ?
D: That 's EDU .
E: That 's a c
D: e e Our e e e
A: But wh I mean E: That 's D: You .
D: We .
D: Us .
A: But what is it ?
D: Well , in the moment it 's a Bayes - net .
D: And it has sort of fifty not - yet - specified interfaces .
D: OK . Eh I have taken care that we actually can build little interfaces , <nonvocalsound> to other modules that will tell us whether the user likes these things and , n the or these things ,
D: and he whether he 's in a wheelchair or not ,
A: OK .
A: Is that supposed to be the international sign for interface ?
D: I think so , yeah .
A: Mmm . OK .
B: I 'd I 'd never seen it before either .
A: OK . Just t
A: Cool .
D: Mmm . So .
E: Cuz things fit onto that , see ?
A: Yeah .
A: Cool .
E: In a vaguely obscene fashion .
D: No , this is a RME core by agent design ,
D: I don't know .
A: That 's so great .
D: There 's maybe a different
E: So wait ,
E: what a what are these letters again ,
E: Situr - <comment> Situation , User , Discourse and
D: Situation , user , d ontology .
A: User ?
E: Ontology .
A: What about the utterance ?
D: That 's here .
E: It 's C: Discourse .
A: Oh , discourse .
D: Yeah .
E: Discourse is all things linguistic , yeah .
A: So that 's not like context , OK .
D: So this this includes the the current utterance plus all the previous utterances .
A: Interesting ,
A: uh - huh .
A: User .
D: And for example w i s I Irena Gurevich is going to be here eh , end of July .
A: User .
D: She 's a new linguist working for EML .
D: And what she would like to do for example is great for us .
D: She would like to take the ent ontolog
C: Ouch .
D: So ,
D: we have discussed in terms of the EVA A: Grateful for us ?
D: uh A: Did you just say grateful for us ?
A: OK , sorry .
A: Anyway .
D: Think of back at the EVA vector ,
D: and Johno coming up with the idea that if the person discussed the discussed the admission fee , in eh previously , that might be a good indication that , " how do I get to the castle ? " , actually he wants to enter .
A: Mm - hmm .
D: Or , you know , " how do I get to X ? "
D: discussing the admission fee in the previous utterance , is a good indication .
E: Mm - hmm .
D: So
D: we don't want a hard code , a set of lexemes , or things , that person 's you know , sort of filter , or uh search the discourse history .
A: Mm - hmm .
D: So what would be kind of cool is that if we encounter concepts that are castle , tower , bank , hotel , we run it through the ontology ,
D: and the ontology tells us it has um , admission , opening times ,
D: it has admission fees ,
D: it has this , it has that ,
D: and then we we we make a thesaurus lexicon , look up ,
D: and then search dynamically through the uh , discourse history for occurrences of these things in a given window of utterances .
A: Mm - hmm .
D: And that might , you know , give us additional input to belief A versus B .
D: Or E versus A .
A: So it 's not just a particular word 's A: OK , so the you 're looking for a few keys that you know are cues to sorry , a few specific cues to some intention .
B: You can dynamically look up keys , yeah .
D: Yeah .
E: Uh , so , wait so um , since this since this sort of technical stuff is going over my head ,
A: OK .
B: And then grep , basically .
E: the the point is that you uh that when someone 's talking about a castle , you know that it 's the sort of thing that people are likely to wanna go into ?
E: Or , is it the fact that if there 's an admission fee , then one of the things we know about admission fees is that you pay them in order to go in ?
E: And then the idea of entering is active in the discourse or something ? And then
D: Well
E: blah - blah - blah ?
E: I mean .
D: the the idea is even more general .
D: The idea is to say , we encounter a certain entity in a in a in a utterance .
D: So le let 's look up everything we the ontology gives us about that entity ,
D: what stuff it does , what roles it has ,
D: what parts , whatever it has .
D: Functions .
D: And , then we look in the discourse , whether any of that , or any surface structure corresponding to these roles , functions aaa <comment> has ever occurred .
E: Oh , OK .
D: And then , the discourse history can t tell us , " yeah " , or " no " .
E: OK .
D: And then it 's up for us to decide what to do with it .
E: OK .
D: t So i
E: So E: No , go ahead .
D: So , we may think that if you say um ,  " where is the theater " , um , whether or not he has talked about tickets before , then we he 's probably wanna go there to see something .
E: Mm - hmm .
E: OK .
D: Or " where is the opera in Par - Paris ? ,
D: yeah ?
D: Lots of people go to the opera to take pictures of it and to look at it ,
E: Mm - hmm . OK .
D: and lots of people go to attend a performance .
E: Mm - hmm .
D: And , the discourse can maybe tell us w what 's more likely if we know what to look for in previous statements .
D: And so we can hard code " for opera , look for tickets , look for this , look for that ,
E: OK .
E: OK .
D: or look for Mozart , look for thi "
D: but the smarter way is to go via the ontology
D: and dynamically , then look up u stuff .
E: OK . But you 're still doing look up so that when the person So the point is that when the person says , " where is it ? " then you sort of say , let 's go back and look at other things and then decide ,
E: rather than the other possibility which is that all through discourse as they talk about different things E: You know like w
E: prior to the " where is it " question they say , you know , " how much does it cost to get in , you know , to to see a movie around here " ,
E: um , " where is the closest theater " E: The the the point is that by mentioning admission fees , that just sort of stays active now .
D: Yeah .
E: You know . That becomes part of like , their sort of current ongoing active conceptual structure .
D: Mm - hmm .
E: And then , um , over in your Bayes - net or whatever , when when the person says " where is it " , you 've already got , you know since they were talking about admission , and that evokes the idea of entering , um , then when they go and ask " where is it " , then you 're Enter node is already active
E: because that 's what the person is thinking about .
D: Mm - hmm .
D: Yeah .
E: I mean that 's the sort of cognitive linguistic - y way ,
E: and probably not practical .
D: Yeah , e ultimately that 's also what we wanna get at .
D: I think that 's that 's the correct way .
D: So , of course we have to keep memory of what was the last intention ,
E: Mm - hmm .
D: and how does it fit to this , and what does it tell us , in terms of of the the what we 're examining .
E: Mmm , yeah .
D: And furthermore , I mean we can idealize that , you know , people don't change topics ,
E: Mm - hmm .
D: but they do .
D: But , even th for that , there is a student of ours who 's doing a dialogue act um , recognition module .
E: Right .
E: Mm - hmm .
D: So , maybe , we 're even in a position where we can take your approach ,
D: which is of course much better , as to say how how do these pieces E: Mmm .
E: And much harder to r program .
D: Hmm ?
E: And much harder to p to program .
D: Yeah . How how do these pieces fit together ?
D: Uh - huh .
D: And um .
D: But , OK , nevertheless . So these are issues
D: but we what we actually decided last week , is to , and this is , again , for your benefit is to um , pretend we have observed and parsed an utterance such as " where is the Powder - Tower " ,
D: or " where is the zoo " ,
D: and specify um , what what we think the the output uh , observe , out i input nodes for our Bayes - nets for the sub sub - D , for the discourse bit , should be .
D: So that And I will I will then <comment> come up with the ontology side uh , bits and pieces ,
D: so that we can say , OK we we always just look at this utterance .
D: That 's the only utterance we can do ,
D: it 's hard coded ,
D: like Srini , sort of hand parsed ,
D: hand crafted ,
D: but this is what we hope to be able to observe in general from utterances , and from ontologies ,
D: and then we can sort of fiddle with these things to see what it actually produces , in terms of output .
E: Uh
D: So we need to find out what the " where is X " construction will give us in terms of semantics
D: and Simspec type things .
A: Just A: OK . Just " where is X " ?
D: Mm - hmm .
A: Or any variants of that .
D: Yeah .
D: No !
D: Um , look at it this way ,
D: i
D: Yeah .
D: What did we decide .
D: We decided sort of the the prototypical " where is X " , where you know , we don't really know , does he wanna go there , or just wanna know where it is .
E: Well we were
D: So the difference of " where is the railway station " , versus where where " where is Greenland " . Nuh ?
E: Mm - hmm .
B: Uh s I was just dancing , sorry .
D: We 're not videotaping any of this .
D: So .
B: Uh ah E: So , um , we 're supposed to E: I mean we 're talking about sort of anything that has the semantics of request for location , right ?
E: actually ?
E: Or ,
E: I mean , anyway , the node in the uh the ultimate , uh , in in the Bayes - net thing when you 're done , the the node that we 're talking about um , is one that says " request for location , true " , or something like that , right ?
E: Um , and and exactly how that gets activated , you know , like whether we want the sentence " how do I get there ? " to activate that node or not , you know , that 's that 's sort of the issue that sort of the linguistic - y side has to deal with , right ?
D: Yeah , but it D: Yea - Nnn
D: Well actually more m more the other way around .
D: We wanted something that represents uncertainty uh we in terms of going there or just wanting to know where it is , for example .
D: Some generic information .
E: OK .
D: And so this is prototypically @ @ found in the " where is something " question , surface structure ,
E: OK .
B: We
D: which can be p you know , should be maps to something that activates both .
D: I mean the idea is to B: I don't E: Alright , OK .
B: Hhh .
D: let 's have it fit nicely with the paper .
B: I guess . I don't B: I don't see unde how we would be able to distinguish between the two intentions just from the g utterance , though .
D: The B: I mean , uh bef or , before we don't before we cranked it through the Bayes - net .
B: I mean .
D: Yeah , we we wouldn't .
D: That 's exactly what we want .
D: We want to get B: We would ?
D: No . We wouldn't .
B: OK ,
B: but then so basically it 's just a for every construction we have a node in the net , right ?
B: And we turn on that node .
D: Yeah .
E: Oy .
D: What what is this gonna D: Exactly .
D: What is the uh D: Well B: And then given that we know that the construction has these two things , we can set up probabilities B: we can s basically define all the tables for ev for those D: Yeah , it should be So we have
D: um ,
D: i let 's assume we we call something like a loc - X node and a path - X node .
D: And what we actually get if we just look at the discourse , " where is X " should activate
D: or should E: Mmm .
D: Hmm . Should be both ,
D: whereas maybe " where is X located " , we find from the data , is always just asked when the person wants to know where it is ,
D: and " how do I get to " is always asked when the person just wants to know how to get there .
D: Right ?
D: So we want to sort of come up with what gets uh , input , and how inter in case of a " where is " question .
D: So what what would the outcome of of your parser look like ?
D: And , what other discourse information from the discourse history could we hope to get , squeeze out of that utterance ?
D: So define the the input into the Bayes - net based on what the utterance , " where is X " , gives us .
D: So definitely have an Entity node here which is activated via the ontology ,
A: s
D: so " where is X " produces something that is s stands for X , whether it 's castle , bank , restroom , toilet , whatever .
D: And then the ontology will tell us A: That it has a location or something like that ? A: or th the ontology will tell us where actually it is located ?
D: No . Not at all .
A: OK .
D: Where it is located , we have , a user proximity node here somewhere ,
A: OK . OK .
D: e which tells us how far the user how far away the user is in respect to that uh entity .
A: OK .
A: So you 're talking about , for instance , the construction obviously involves this entity or refers refers to this entity ,
D: Mm - hmm .
A: and from the construction also you know that it is a location A: is or a thing thing that can be located .
A: Right ? Ontology says this thing has a location slot .
A: Sh - and that 's the thing that is being that is the content of the question that 's being queried by one interpretation of " where is X " .
A: And another one is , um , path from current user current location to <comment> that location .
D: Mm - hmm .
A: So .
A: So is the question A: I mean it 's just that I 'm not sure what the A: Is the question , for this particular construction how we specify that that 's the information it provides ?
A: Or or asked for ?
A: b Both sides , right ?
D: Yeah , you don't need to even do that .
D: It 's just sort of what what would be @ @ <comment> observed in uh in that case .
A: Observed when you heard the speaker say " where is X " , or when when that 's been parsed ?
D: Mm - hmm .
A: So these little circles you have by the D ?
A: Is that ?
D: That 's exactly what we 're looking for .
A: OK . OK .
B: I d I just I don't like having characterizing the constructions with location and path ,
B: or li characterizing them like that .
B: Cuz you don't It seems like in the general case you wouldn't know how how to characterize them .
D: You wouldn't .
B: I mean or , for when .
B: There could be an interpretation that we don't have a node for in the B: I mean it just seems like @ @ has to have uh a node for the construction and then let the chips fall where they may .
B: Versus uh , saying , this construction either can mean location or path .
B: And , in this cas and since since it can mean either of those things , it would light both of those up .
D: It 's the same .
B: Thoughts ?
B: Questions ?
E: I 'm thinking about it .
E: Um D: It will be the same .
D: So I think r in here we have " I 'll go there " , right ?
B: Answers ?
D: And we have our Info - on .
D: So in my c my case , this would sort of make this happy , and this would make the Go - there happy .
D: What you 're saying is we have a Where - X question , Where - X node , that makes both happy .
D: Right ?
D: That 's what you 're proposing , which is , in my mind
D: just as fine .
D: So w if we have a construction node , " where is X " , it 's gonna both get the po posterior probability that it 's Info - on up ,
B: Mmm , yeah .
D: Info - on is True - up , and that Go - there is True - up , as well .
D: Which would be exactly analogous to what I 'm proposing is , this makes uh makes something here true ,
D: and this makes something also something here true ,
D: and this makes this True - up , and this makes this True - up as well .
E: I kinda like it better without that extra level of indirection too .
E: You know with with this points to this points to that , and so on
E: because I don't know , it D: Yeah , because we get we get tons of constructions I think .
A: Is - uh ,
E: Yeah .
D: Because , you know , mmm people have many ways of asking for the same thing ,
B: Yeah , sure .
A: Yeah .
D: and A: So un
B: I change I changed my mind actually .
E: OK .
A: So I agree with that .
A: I have a different kinda question ,
A: might be related ,
A: which is , OK so implicitly everything in EDU , we 're always inferring the speaker intent , right ?
A: Like , what they want
A: either , the information that they want , or A: It 's always information that they want probably , of some kind .
A: Right ?
A: Or I I don't know ,
D: The system doesn't massage you , no . No .
A: or what 's something that they A: I I I don't A: OK .
A: So , um ,
A: let 's see .
A: So I don't know if the I mean i if th just there 's more s here that 's not shown that you it 's already like part of the system whatever ,
A: but , " where is X " , like , the fact that it is , you know , a speech - act , whatever ,
A: it is a question .
A: It 's a question that , um , queries on some particular thing X ,
A: and X is that location .
A: There 's , like , a lot of structure in representing that .
D: Yep . Yeah .
A: So that seems different from just having the node " location - X "
A: and that goes into EDU , right ?
D: Yeah . Precisely .