Grad D: Mm - hmm .
Grad A: And people will sort of figure out or ask about the bits that are implicit .
Grad D: Yeah . So this will be sort of documenting what we think , and documenting what we have in terms of the Bayes - net stuff .
Grad A: Mm - hmm .
Grad D: And since there 's never a bad idea to document things , no ?
Grad A: That 's th that 's definitely a good idea .
Grad D: That would be my , uh We we should sketch out the details maybe tomorrow afternoon - ish , if everyone is around . I don't know . You probably wouldn't be part of it .
Grad E: I think so .
Grad D: Maybe you want ? Think about it . Um , You may may ruin your career forever , if you appear .
Grad B: Yeah , you might get blacklisted .
Grad D: And um , the uh , other thing , yeah we actually Have we made any progress on what we decided , uh , last week ? I 'm sure you read the transcript of last week 's meeting in red so sh so you 're up to dated caught up .
Grad A: No . Sorry .
Grad D: We decided t that we 're gonna take a " where is something " question , and pretend we have parsed it , and see what we could possibly hope to observe on the discourse side .
Grad B: Remember I came in and I started asking you about how we were sor going to sort out the uh , decision nodes ?
Grad A: Yes ! What 'd you say ?
Grad B: I remember you talking to me , just not what you said .
Grad A: I do remember you talking to me . Um , a few more bits .
Grad B: Well , there was like we needed to or uh , in my opinion we need to design a Bayes another sub - Bayes - net You know , it was whether it was whether we would have a Bayes - net on the output and on the input ,
Grad A: Oh .
Grad B: or whether the construction was gonna be in the Bayes - net ,
Grad A: Oh , yeah . OK .
Grad B: a and outside of it ,
Grad A: OK .
Grad B: and Grad A: So that was was that the question ? Was that what Grad B: Well that was related to what we were talking about .
Grad D: Should I introduce it as SUDO - square ?
Grad B: Yeah sure .
Grad D: We have to put this in the paper . If we write it . This is this is my only constraint . The th So . The SUDO - square {nonvocalsound} is , " Situation " , " User " , " Discourse " , right ? " Ontology " .
Grad E: Oh I saw the diagram in the office ,
Grad A: Oh my god , that 's amazing !
Grad D: Mmm . Yeah . Whatever .
Grad A: No way .
Grad E: Way !
Grad D: Is it ?
Grad A: Someone 's gonna start making Phil Collins jokes .
Grad D: Yeah . Hmm ?
Grad A: Sorry .
Grad B: What ?
Grad E: Oh , god , I hope not .
Grad A: You guys are too young .
Grad E: You know like " Sussudio " ,
Grad A: Yeah , come on .
Grad E: that horrible , horrible song that should never have been created .
Grad B: Oh , oh , oh , oh .
Grad A: I know , that was horrible . Sussudio .
Grad B: I 've blocked every aspect of Phil Collins out of my mind .
Grad C: What ?
Grad A: I 'm sorry , I haven't . Not on purpose .
Grad E: in here
Grad D: Oh  Well , also he 's talking about suicide , and that 's that 's not a notion I wanna have evoked .
Grad A: No , he 's not . Really ?
Grad D: He is .
Grad A: Oops . {comment} I didn't really listen to it ,
Grad D: The Grad A: I was too young .
Grad E: Hmm .
Grad A: Anyway .
Grad E: It sounds too rocking for that .
Grad A: Yeah .
Grad E: Anyway . So , what 's going on here ? So what are what Grad D: So ,
Grad E: Was wollte der Kuenstler uns damit sagen ?
Grad A: Stop excluding me .
Grad D: OK , so we have tons of little things here ,
Grad A: I can't believe that that 's never been thought of before .
Grad D: and we 've
Grad B: Wait , what are the dots ? I don't remember what the dots were .
Grad E: Those are little bugs .
Grad A: Cool Keith .
Grad D: OK .
Grad A :
Grad D: You know , these are our , whatever , belief - net decision nodes , and they all contribute to these {nonvocalsound} things down here .
Grad B: Oh , oh .
Grad A: Wait , wait , what 's the middle thing ?
Grad D: That 's EDU .
Grad E: That 's a c
Grad D: e e Our e e e
Grad A: But wh I mean Grad E: That 's Grad D: You . We . Us .
Grad A: But what is it ?
Grad D: Well , in the moment it 's a Bayes - net . And it has sort of fifty not - yet - specified interfaces . 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 , and he whether he 's in a wheelchair or not ,
Grad A: OK . Is that supposed to be the international sign for interface ?
Grad D: I think so , yeah .
Grad A: Mmm . OK .
Grad B: I 'd I 'd never seen it before either .
Grad A: OK . Just t Cool .
Grad D: Mmm . So .
Grad A: Yeah .
Grad E: Cuz things fit onto that , see ?
Grad A: Cool .
Grad E: In a vaguely obscene fashion .
Grad D: No , this is a RME core by agent design , I don't know .
Grad A: That 's so great .
Grad D: There 's maybe a different
Grad E: So wait , what a what are these letters again , Situr - {comment} Situation , User , Discourse and
Grad D: Situation , user , d ontology .
Grad A: User ?
Grad E: Ontology .
Grad A: What about the utterance ?
Grad C: Discourse .
Grad D: That 's here .
Grad E: It 's Grad A: Oh , discourse . So that 's not like context , OK .
Grad D: Yeah .
Grad E: Discourse is all things linguistic , yeah .
Grad D: So this this includes the the current utterance plus all the previous utterances .
Grad A: Interesting , uh - huh . User .
Grad D: And for example w i s I Irena Gurevich is going to be here eh , end of July .
Grad A: User .
Grad D: She 's a new linguist working for EML . And what she would like to do for example is great for us . She would like to take the ent ontolog
Grad C: Ouch .
Grad D: So , we have discussed in terms of the EVA Grad A: Grateful for us ?
Grad D: uh Grad A: Did you just say grateful for us ? OK , sorry . Anyway .
Grad D: Think of back at the EVA vector , 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 .
Grad A: Mm - hmm .
Grad D: Or , you know , " how do I get to X ? " discussing the admission fee in the previous utterance , is a good indication .
Grad E: Mm - hmm .
Grad A :
Grad D: So 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 .
Grad A: Mm - hmm .
Grad 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 , and the ontology tells us it has um , admission , opening times , it has admission fees , it has this , it has that , and then we we we make a thesaurus lexicon , look up , and then search dynamically through the uh , discourse history for occurrences of these things in a given window of utterances .
Grad A: Mm - hmm .
Grad D: And that might , you know , give us additional input to belief A versus B . Or E versus A .
Grad A: So it 's not just a particular word 's OK , so the you 're looking for a few keys that you know are cues to sorry , a few specific cues to some intention .
Grad B: You can dynamically look up keys , yeah .
Grad D: Yeah .
Grad A: OK .
Grad E: Uh , so , wait so um , since this since this sort of technical stuff is going over my head ,
Grad B: And then grep , basically .
Grad 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 ? 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 ? And then the idea of entering is active in the discourse or something ? And then
Grad D: Well
Grad E: blah - blah - blah ?
Grad D: the the idea is even more general .
Grad E: I mean .
Grad D: The idea is to say , we encounter a certain entity in a in a in a utterance . So le let 's look up everything we the ontology gives us about that entity , what stuff it does , what roles it has , what parts , whatever it has . Functions . 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 .
Grad E: Oh , OK .
Grad D: And then , the discourse history can t tell us , " yeah " , or " no " .
Grad E: OK .
Grad D: And then it 's up for us to decide what to do with it . t So i
Grad E: OK . So No , go ahead .
Grad 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 .
Grad E: Mm - hmm .
Grad D: Or " where is the opera in Par - Paris ? ,
Grad E: OK .
Grad D: yeah ? Lots of people go to the opera to take pictures of it and to look at it ,
Grad E: Mm - hmm . OK .
Grad D: and lots of people go to attend a performance .
Grad E: Mm - hmm .
Grad D: And , the discourse can maybe tell us w what 's more likely if we know what to look for in previous statements . And so we can hard code " for opera , look for tickets , look for this , look for that ,
Grad E: OK . OK .
Grad D: or look for Mozart , look for thi " but the smarter way is to go via the ontology and dynamically , then look up u stuff .
Grad 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 , rather than the other possibility which is that all through discourse as they talk about different things You know like w 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 " , um , " where is the closest theater " The the the point is that by mentioning admission fees , that just sort of stays active now .
Grad D: Yeah .
Grad E: You know . That becomes part of like , their sort of current ongoing active conceptual structure .
Grad D: Mm - hmm .
Grad 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
Grad D: Mm - hmm .
Grad E: because that 's what the person is thinking about .
Grad D: Yeah .
Grad E: I mean that 's the sort of cognitive linguistic - y way ,
Grad D: Yeah , e ultimately that 's also what we wanna get at .
Grad E: and probably not practical .
Grad D: I think that 's that 's the correct way . So , of course we have to keep memory of what was the last intention , and how does it fit to this , and what does it tell us , in terms of of the the what we 're examining .
Grad E: Mm - hmm . Mmm , yeah .
Grad D: And furthermore , I mean we can idealize that , you know , people don't change topics ,
Grad E: Mm - hmm .
Grad D: but they do . But , even th for that , there is a student of ours who 's doing a dialogue act um , recognition module .
Grad E: Right . Mm - hmm .
Grad D: So , maybe , we 're even in a position where we can take your approach , which is of course much better , as to say how how do these pieces Grad E: Mmm . And much harder to r program .
Grad D: Hmm ?
Grad E: And much harder to p to program .
Grad D: Yeah . How how do these pieces fit together ? Uh - huh . And um . But , OK , nevertheless . So these are issues 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 " , or " where is the zoo " , 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 . So that And I will I will then {comment} come up with the ontology side uh , bits and pieces , so that we can say , OK we we always just look at this utterance . That 's the only utterance we can do , it 's hard coded , like Srini , sort of hand parsed , hand crafted , but this is what we hope to be able to observe in general from utterances , and from ontologies , and then we can sort of fiddle with these things to see what it actually produces , in terms of output .
Grad E: Uh
Grad D: So we need to find out what the " where is X " construction will give us in terms of semantics and Simspec type things .
Grad A: Just OK . Just " where is X " ?
Grad D: Mm - hmm .
Grad A: Or any variants of that .
Grad D: Yeah . No ! Um , look at it this way , i Yeah . What did we decide . 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 .
Grad E: Well we were
Grad D: So the difference of " where is the railway station " , versus where where " where is Greenland " . Nuh ?
Grad E: Mm - hmm .
Grad B: Uh s I was just dancing , sorry .
Grad D: We 're not videotaping any of this . So .
Grad B: Uh ah Grad E: So , um , we 're supposed to I mean we 're talking about sort of anything that has the semantics of request for location , right ? actually ? Or , 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 ? 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 ?
Grad D: Yeah , but it Yea - Nnn Well actually more m more the other way around . We wanted something that represents uncertainty uh we in terms of going there or just wanting to know where it is , for example . Some generic information .
Grad E: OK .
Grad D: And so this is prototypically @ @ found in the " where is something " question , surface structure ,
Grad E: OK .
Grad B: We
Grad D: which can be p you know , should be maps to something that activates both . I mean the idea is to Grad B: I don't Grad E: Alright , OK .
Grad B: Hhh . I guess . I don't Grad D: let 's have it fit nicely with the paper .
Grad B: I don't see unde how we would be able to distinguish between the two intentions just from the g utterance , though .
Grad D: The Grad B: I mean , uh bef or , before we don't before we cranked it through the Bayes - net . I mean .
Grad D: Yeah , we we wouldn't . That 's exactly what we want .
Grad B: We would ?
Grad D: We want to get No . We wouldn't .
Grad B: OK , but then so basically it 's just a for every construction we have a node in the net , right ? And we turn on that node .
Grad D: Yeah . What what is this gonna Grad E: Oy .
Grad D: Exactly . What is the uh Well Grad B: And then given that we know that the construction has these two things , we can set up probabilities we can s basically define all the tables for ev for those Grad D: Yeah , it should be So we have um , i let 's assume we we call something like a loc - X node and a path - X node . And what we actually get if we just look at the discourse , " where is X " should activate or should Grad E: Mmm .
Grad D: Hmm . Should be both , whereas maybe " where is X located " , we find from the data , is always just asked when the person wants to know where it is , and " how do I get to " is always asked when the person just wants to know how to get there . Right ? So we want to sort of come up with what gets uh , input , and how inter in case of a " where is " question . So what what would the outcome of of your parser look like ? And , what other discourse information from the discourse history could we hope to get , squeeze out of that utterance ? So define the the input into the Bayes - net based on what the utterance , " where is X " , gives us . So definitely have an Entity node here which is activated via the ontology ,
Grad A: s
Grad D: so " where is X " produces something that is s stands for X , whether it 's castle , bank , restroom , toilet , whatever . And then the ontology will tell us Grad A: That it has a location or something like that ? or th the ontology will tell us where actually it is located ?
Grad D: No . Not at all .
Grad A: OK .
Grad D: Where it is located , we have , a user proximity node here somewhere ,
Grad A: OK . OK .
Grad D: e which tells us how far the user how far away the user is in respect to that uh entity .
Grad A: OK . So you 're talking about , for instance , the construction obviously involves this entity or refers refers to this entity ,
Grad D: Mm - hmm .
Grad A: and from the construction also you know that it is a location is or a thing thing that can be located . Right ? Ontology says this thing has a location slot . 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 " . And another one is , um , path from current user current location to {comment} that location .
Grad D: Mm - hmm .
Grad A: So . So is the question I mean it 's just that I 'm not sure what the Is the question , for this particular construction how we specify that that 's the information it provides ? Or or asked for ? b Both sides , right ?
Grad D: Yeah , you don't need to even do that . It 's just sort of what what would be @ @ {comment} observed in uh in that case .
Grad A: Observed when you heard the speaker say " where is X " , or when when that 's been parsed ?
Grad D: Mm - hmm .
Grad A: So these little circles you have by the D ? Is that ? OK . OK .
Grad D: That 's exactly what we 're looking for .
Grad B: I d I just I don't like having characterizing the constructions with location and path , or li characterizing them like that . Cuz you don't It seems like in the general case you wouldn't know how how to characterize them .
Grad D: You wouldn't .
Grad B: I mean or , for when . There could be an interpretation that we don't have a node for in the I mean it just seems like @ @ has to have uh a node for the construction and then let the chips fall where they may . Versus uh , saying , this construction either can mean location or path . And , in this cas and since since it can mean either of those things , it would light both of those up .
Grad D: It 's the same .
Grad B: Thoughts ? Questions ?
Grad E: I 'm thinking about it .
Grad D: It will be the same .
Grad E: Um Grad D: So I think r in here we have " I 'll go there " , right ?
Grad B: Answers ?
Grad D: And we have our Info - on . So in my c my case , this would sort of make this happy , and this would make the Go - there happy . What you 're saying is we have a Where - X question , Where - X node , that makes both happy . Right ? That 's what you 're proposing , which is , in my mind just as fine . 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 ,
Grad B: Mmm , yeah .
Grad D: Info - on is True - up , and that Go - there is True - up , as well . Which would be exactly analogous to what I 'm proposing is , this makes uh makes something here true , and this makes something also something here true , and this makes this True - up , and this makes this True - up as well .
Grad E: I kinda like it better without that extra level of indirection too . You know with with this points to this points to that , and so on because I don't know , it Grad A: Is - uh ,
Grad D: Yeah , because we get we get tons of constructions I think . Because , you know , mmm people have many ways of asking for the same thing ,
Grad E: Yeah .
Grad B: Yeah , sure .
Grad A: Yeah .
Grad D: and Grad A: So un
Grad B: I change I changed my mind actually .
Grad A: So I agree with that .
Grad E: OK .
Grad A: I have a different kinda question , might be related , which is , OK so implicitly everything in EDU , we 're always inferring the speaker intent , right ? Like , what they want either , the information that they want , or It 's always information that they want probably , of some kind . Right ? Or I I don't know , or what 's something that they Grad D: The system doesn't massage you , no . No .
Grad A: I I I don't OK . So , um , let 's see . 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 , but , " where is X " , like , the fact that it is , you know , a speech - act , whatever , it is a question . It 's a question that , um , queries on some particular thing X , and X is that location . There 's , like , a lot of structure in representing that .
Grad D: Yep . Yeah .
Grad A: So that seems different from just having the node " location - X " and that goes into EDU , right ?
Grad D: Yeah . Precisely . That 's that 's Grad A: So tha is that what you 're t talking about ?
Grad D: So , w Exactly . We have su we have specified two .
Grad A: wh what kinds of structure we want .
Grad D: OK , the next one would be here , just for mood .
Grad E: Mm - hmm .
Grad A: Mm - hmm .
Grad D: The next one would be what we can squeeze out of the uh I don't know , maybe we wanna observe the uh , um ,  uh the length of of the words used , and , or the prosody
Grad A: Mmm .
Grad D: and g a and t make conclusions about the user 's intelligence .
Grad A: OK . So in some ways Grad D: I don't know ,
Grad A: um , so in some ways in the other sort of parallel set of mo more linguistic meetings we 've been talking about possible semantics of some construction .
Grad D: yeah .
Grad E: Mm - hmm .
Grad A: Right ? Where it was the simulation that 's , according to it you know , that that corresponds to it , and as well the as discourse , whatever , conte infor in discourse information ,
Grad D: Mm - hmm .
Grad A: such as the mood , and , you know , other stuff . So , are we looking for a sort of abbreviation of that , that 's tailored to this problem ? Cuz that that has , you know , basically , you know , s it 's in progress still it 's in development still , but it definitely has various feature slots , attributes , um , bindings between things Grad D: Mm - hmm . Yeah . U that 's exactly r um , why I 'm proposing It 's too early to have to think of them of all of these discourse things that one could possibly observe ,
Grad A: Uh - huh . Mm - hmm .
Grad D: so let 's just assume
Grad A: For the subset of Grad D: human beings are not allowed to ask anything but " where is X " .
Grad A: OK .
Grad D: This is the only utterance in the world . What could we observe from that ?
Grad A: OK . That exactly " where is X " ,
Grad D: In ter
Grad A: not the the choices of " where is X " or " how do I get to X " . Just " where is X " .
Grad D: Just just " where is X " .
Grad E: Yeah .
Grad A: OK .
Grad D: And , but you know , do it do it in such a way that we know that people can also say , " is the town hall in front of the bank " , so that we need something like a w WH focus . Nuh ? Should be should be there , that , you know , this the whatever we get from the Grad A: Wait , so do , or do not take other kinds of constructions into account ?
Grad D: Well , if you if you can , oh definitely do ,
Grad A: OK . Where possible . OK .
Grad D: where possible . Right ? If i if if it 's not at all triggered by our thing , then it 's irrelevant ,
Grad A: Mm - hmm .
Grad D: and it doesn't hurt to leave it out for the moment . Um , but Grad A: OK . Um , it seems like for instance , " where is X " , the fact that it might mean um , " tell me how to get to X " , like Do y So , would you wanna say that those two are both , like Those are the two interpretations , right ? the the ones that are location or path . So , you could say that the s construction is a question asking about this location , and then you can additionally infer , if they 're asking about the location , it 's because they wanna go to that place , in which case , the you 're jumping a step step and saying , " oh , I know where it is
Grad D: Mm - hmm .
Grad E: Yeah .
Grad A: but I also know how to get they wanna seem they seem to wanna get there so I 'm gonna tell them " . So there 's like structure
Grad E: Right , th this it 's not it 's not that this is sort of like semantically ambiguous between these two .
Grad A: i do you kn sort of uh , that Grad D: Mm - hmm .
Grad E: It 's really about this but why would you care about this ? Well , it 's because you also want to know this , or something like that right ?
Grad A: So it 's like you infer the speaker intent ,
Grad D: Mm - hmm .
Grad A: and then infer a plan , a larger plan from that , for which you have the additional information ,
Grad E: Yeah .
Grad D: Mm - hmm .
Grad A: you 're just being extra helpful .
Grad D: Yep .
Grad A: Um .
Grad D: Think Uh , well this is just a mental exercise .
Grad A: Yeah .
Grad D: If you think about , focus on this question , how would you design that ?
Grad E: Mm - hmm .
Grad D: Is it do you feel confident about saying this is part of the language already to to detect those plans , and why would anyone care about location , if not , you know and so forth .
Grad E: Mmm .
Grad D: Or do you actually , I mean this is perfectly legitimate , and I I would not have any problems with erasing this and say , that 's all we can activate , based on the utterance out of context .
Grad A: Mm - hmm . And just by an additional link Oh .
Grad D: What ?
Grad A: Right ,
Grad E: Right .
Grad A: like ,
Grad D: And then the the the miracle that we get out the intention , Go - there , happens , based on what we know about that entity , about the user , about his various beliefs , goals , desires , blah - blah - blah .
Grad A: with context and enough user information , yeah .
Grad E: Yeah .
Grad D: Absolutely fine . But this is the sort of thing , I I propose that we think about ,
Grad A: OK .
Grad D: so that we actually end up with um , um , nodes for the discourse and ontology so that we can put them into our Bayes - net , never change them , so we all there is is " where is X " , and , Eva can play around with the observed things , and we can run our better JavaBayes , and have it produce some output . And for the first time in th in in the world , we look at our output , and um and see uh whether it it 's any good .
Grad A: OK .
Grad D: You know ? I mean ,
Grad E: Here 's hoping .
Grad D: Hmm ?
Grad E: Here 's hoping . Right ? Now cross your fingers .
Grad D: Yeah , I I mean , for me this is just a ba matter of curiosity , I wanna would like to look at uh , what this ad - hoc process of designing a belief - net would actually produce .
Grad E: Yeah . {comment} Yeah .
Grad A: Mmm .
Grad D: If if we ask it where is something . And , maybe it also h enables you to think about certain things more specifically , um , come up with interesting questions , to which you can find interesting answers . And , additionally it might fit in really nicely with the paper . Because if if if we want an example for the paper , I suggest there it is .
Grad E: Um - hmm . Yeah .
Grad D: So th this might be a nice opening paragraph for the paper as saying , " you know people look at kinds of  at ambiguities " , and um , in the literature there 's " bank " and whatever kinds of garden path phenomenon .
Grad E: Mm - hmm .
Grad D: And we can say , well , that 's all nonsense . A , A , uh these things are never really ambiguous in discourse , B , B , don't ever occur really in discourse , but normal statements that seem completely unambiguous , such as " where is the blah - blah " , actually are terribly complex , and completely ambiguous .
Grad E: Mm - hmm . Mm - hmm .
Grad D: And so , what every everybody else has been doing so far in in in you know , has been completely nonsensical , and can all go into the wastepaper bin , and the only Grad E: That 's always a good way to begin . Yeah . Yeah .
Grad D: Yeah . And the the the only Grad B: I am great .
Grad D: Yeah .
Grad E: All others are useless .
Grad D: Yeah .
Grad E: That 's good .
Grad D: Nice overture , but , you know , just not really OK , I 'm eja exaggerating , but that might be , you know , saying " hey " , you know , some stuff is is actually complex , if you look at it in in in the vacuum
Grad E: Mm - hmm .
Grad D: and and ceases to be complex in reality . And some stuff that 's as that 's absolutely straightforward in the vacuum , is actually terribly complex in reality . Would be nice sort of , uh , also , nice , um bottom - up linguistics , um , type message .
Grad E: Mm - hmm . True .
Grad D: Versus the old top - down school . I 'm running out of time . OK .
Grad B: When do you need to start wizarding ?
Grad D: At four ten . OK , this is the other bit of news . The subjects today know Fey , so she can't be here , and do the wizarding . So I 'm gonna do the wizarding
Grad E: Huh .
Grad D: and Thilo 's gonna do the instructing .
Grad B: Mmm .
Grad D: Also we 're getting a a person who just got fired uh , from her job . Uh a person from Oakland who is interested in maybe continuing the wizard bit once Fey leaves in August . And um , she 's gonna look at it today . Which is good news in the sense that if we want to continue , after the thir thir after July , we can . We could . And , um and that 's also maybe interesting for Keith and whoever , if you wanna get some more stuff into the data collection . Remember this , we can completely change the set - up any time we want .
Grad E: Mm - hmm . OK .
Grad D: Look at the results we 've gotten so far for the first , whatever , fifty some subjects ?
Grad A: Fifty ? You 've had fifty so far , or ?
Grad D: No , we 're approaching twenty now .
Grad A: OK .
Grad D: But , until Fey is leaving , we surely will hit the some of the higher numbers .
Grad A: Yeah . Hmm .
Grad D: And um , so that 's cool . Can a do more funky stuff .
Grad E: Sure . Yeah , I 'll have to look more into that data . Is that around ? Like , cuz that 's pretty much getting posted or something right away when you get it ?
Grad D: Um .
Grad E: Or ? I guess it has to be transcribed , huh ?
Grad D: We have uh , eh found someone here who 's hand st hand transcribing the first twelve .
Grad E: OK .
Grad D: First dozen subjects
Grad E: Uh - huh .
Grad D: just so we can build a a language model for the recognizer .
Grad E: OK .
Grad D: But , um So those should be available soon .
Grad E: OK .
Grad D: The first twelve . And I can ch ch st e
Grad E: You know I mean you know that I that I looked at the first the first one and got enough data to keep me going for , you know , probably most of July . So . But , um . Yeah , a probably not the right way to do it actually .
Grad D: But you can listen to a y y y You can listen to all of them from your Solaris box .
Grad E: OK .
Grad D: If you want .
Grad E: Right .
Grad D: It 's always fun .