grad d: I tried to go for the EE Cummings sort of feeling , but grad a: Three three six zero zero . grad a: You ever seen " So I married an axe murderer " ? grad c:  parts of it . grad a: There 's a part wh there 's parts when he 's doing beat poetry . is when he 's he works in a coffee shop , in San Francisco , and  he 's sitting there on this couch and they bring him this massive cup of espresso , and he 's like " excuse me I ordered the large espresso ? " grad d:  . grad a: Wait do are y So you 're trying to decide who 's the best taster of tiramisu ? grad d: No ?  . There was a a a fierce argument that broke out over whose tiramisu might be the best and so we decided to have a contest where those people who claim to make good tiramisu make them , grad a: Ah . grad d: and then we got a panel of impartial judges that will taste do a blind taste and then vote . grad a: Seems like Seems like you could put a s magic special ingredient in , so that everyone know which one was yours . Well , I was thinking if  y you guys have plans for Sunday ? We 're we 're not it 's probably going to be this Sunday , but  we 're sort of working with the weather here because we also want to combine it with some barbecue activity where we just fire it up and what whoever brings whatever you know , can throw it on there . grad a: Well , I 'm going back to visit my parents this weekend , so , I 'll be out of town . grad d: So you 're going to the west Bay then ? No , grad a: No , the South Bay , grad d: south Bay ? grad a: yeah . Wonder if these things ever emit a very , like , piercing screech right in your ear ? grad d: They are gonna get more comfortable headsets . I actually  , even though Liz was  kind enough to offer to be the first subject , I sort of felt that she knew too much , so I asked  Litonya . grad d: So , this is what she saw as part of as  for instr introduction , this is what she had to read aloud .  , that was really difficult for her and  grad c: Because of l all the names , you mean ? grad d: The names and  this was the  first three tasks she had to to master after she called the system , and  then of course the system broke down , and those were the l   I should say the system was supposed to break down and then  these were the remaining three tasks that she was going to solve , with a human  . And now comes the This is the phone - in phase of grad c: Wait , can I I have a question . So there 's no system , right ? Like , there was a wizard for both  both parts , is this right ? grad d: Yeah . grad d: One time , pretending to be a system , one time , to pretending to be a human , which is actually not pretending . Isn't this kind of obvious when it says " OK now you 're talking to a human " and then the human has the same voice ? grad d: No no no . And  the wizard sometimes will not be audible , Because she was actually they there was some  lapse in the  wireless , we have to move her closer . grad a: Is she mispronouncing " Anlage " ? Is it " Anlaga " or " Anlunga " grad d: They 're mispronouncing everything , grad a: OK . Well , if we we  professor b: So , are are you trying to record this meeting ? grad d: There was a strange reflex .  , that was already anticipated by some people suggested that if we just have bullets here , they 're gonna not they 're subjects are probably not gonna going to follow the order . professor b: S so if you just number them " one " , " two " , " three " it 's grad d: Yeah , and make it sort of clear in the  professor b: OK . That is something that Fey actually thought of a in the last second that sh the system should introduce itself , when it 's called . grad d: And  , another suggestion , by Liz , was that we  , through subjects , switch the tasks . So when when they have task - one with the computer , the next person should have task - one with a human , and so forth .  , we have to refine the tasks more and more , which of course we haven't done at all , so far , in order to avoid this rephrasing , so where , even though w we don't tell the person " ask blah - blah - blah - blah - blah " they still try , or at least Litonya tried to  repeat as much of that text as possible . grad d: And  my suggestion is of course we we keep the wizard , because I think she did a wonderful job , professor b: Great . grad d: in the sense that she responded quite nicely to things that were not asked for , " How much is a t a bus ticket and a transfer " so this is gonna happen all the time , we d you can never be sure . grad a: I wasn't wasn't sure whether wizard was the correct term for  " not a man " . grad c: There 's no female equivalent of grad d: But  grad a: Are you sure ? grad c: No , I don't know . grad d: Well , there is witch and warlock , grad a: Yeah , that 's so @ @ . grad c: Yeah , that 's what I was thinking , but grad d: and  professor b: Right . And  So , some some work needs to be done , but I think we can  And this , and in case no you hadn't seen it , this is what Litonya looked at during the   while taking the while partaking in the data collection .  , do you know exactly how to do that , or is  Lila  , you know what exactly do we do to to put her on the payroll in some way ? grad d: I 'm completely clueless , but I 'm willing to learn . So anyway ,  grad d: N professor b: So why don't you  ask Lila and see what she says about you know exactly what we do for someone in th grad d: Student - type worker , professor b: Well , yeah she 's un she 's not a a student , grad d: or ? professor b: she just graduated but anyway . professor b: So i if Yeah , I agree , she sounded fine , she a actually was  , more  , present and stuff than than she was in conversation , so she did a better job than I would have guessed from just talking to her . grad d: This is sort of what I gave her , so this is for example h how to get to the student prison , professor b: Yeah . grad d: and I didn't even spell it out here and in some cases I I spelled it out a little bit  more thoroughly , professor b: Right . grad d: this is the information on on the low sunken castle , and the amphitheater that never came up , and  , so i if we give her even more  , instruments to work with I think the results are gonna be even better . professor b: Oh , yeah , and then of course as she does it she 'll she 'll learn @ @ .  And also if she 's willing to take on the job of organizing all those subjects and stuff that would be wonderful . professor b: And ,  she 's actually she 's going to graduate school in a kind of an experimental paradigm , so I think this is all just fine in terms of h her learning things she 's gonna need to know  , to do her career . professor b: So , I my guess is she 'll be r r quite happy to take on that job . grad d: And  I told her that we gonna  figure out a meeting time in the near future to refine the tasks and s look for the potential sources to find people . She also agrees that you know if it 's all just gonna be students the data is gonna be less valuable because of that so . professor b: Well , as I say there is this s set of people next door , it 's not hard to grad d: We 're already Yeah . professor b:  grad d: However , we may run into a problem with a reading task there . We could talk to the people who run it and  see if they have a way that they could easily  tell people that there 's a task , pays ten bucks or something , grad d:   Now , I signed us up for the Wednesday slot , and part of what we should do is this . professor b: So , my idea on that was  , partly we 'll talk about system stuff for the computer scientists , but partly I did want it to get the linguists involved in some of this issue about what the task is and all  you know , what the dialogue is , and what 's going on linguistically , because to the extent that we can get them contributing , that will be good . professor b: maybe we can get some of the linguists sufficiently interested that they 'll help us with it , other linguists , if you 're a linguist , but in any case , grad d: Yep . So my idea on on Wednesday is partly to  you  , what you did today would i is just fine . You just  do " this is what we did , and here 's the thing , and here 's s some of the dialogue and and so forth . " But then , the other thing of course is we should  give the computer scientists some idea of of what 's going on with the system design , and where we think the belief - nets fit in and where the pieces are and stuff like that . So , I don't I don't think it 's worth a lot of work , particularly on your part , to to to make a big presentation . I don't think you should you don't have to make any new  PowerPoint or anything . The  other two things is  we 've can have Johno tell us a little about this professor b: Great . grad d: and we also have a l little bit on the interface , M - three - L enhancement , and then  that was it , I think . grad a: So , what I did for this this is  , a pedagogical belief - net because I was I I took I tried to conceptually do what you were talking about with the nodes that you could expand out so what I did was I took I made these dummy nodes called Trajector - In and Trajector - Out that would isolate the things related to the trajector . And then I  did similar things for our our net to  with the context and the discourse and whatnot , so we could sort of isolate them or whatever in terms of the the top layer . Let 's go Slide all the way up so we see what the p the p very bottom looks like , or is that it ? grad a: Yeah , there 's just one more node and it says " Mode " which is the decision between the grad d: Yeah . grad a: So basically all I did was I took the last belief - net professor b: So   grad a: and I grouped things according to what how I thought they would fit in to  image schemas that would be related . And the two that I came up with were Trajector - landmark and then Source - path - goal as initial ones . grad a: And then I said well ,  the trajector would be the person in this case probably . grad a: you know , we have we have the concept of what their intention was , whether they were trying to tour or do business or whatever , professor b: Right . And then  in terms of the source , the things  the only things that we had on there I believe were whether Oh actually , I kind of , I might have added these cuz I don't think we talked too much about the source in the old one but  whether the where I 'm currently at is a landmark might have a bearing on whether grad d:   And " usefulness " is basi basically means is that an institutional facility like a town hall or something like that that 's not something that you 'd visit for tourist 's tourism 's sake or whatever . " Travel constraints " would be something like you know , maybe they said they can they only wanna take a bus or something like that , right ? And then those are somewhat related to the path , professor b:   grad a: so that would determine whether we 'd could take we would be telling them to go to the bus stop or versus walking there directly . Similar things as the source except they also added whether the entity was closed and whether they have somehow marked that is was the final destination .  , and then if you go up , Robert , Yeah , so  , in terms of Context , what we had currently said was whether they were a businessman or a tourist of some other person .  , Discourse was related to whether they had asked about open hours or whether they asked about where the entrance was or the admission fee , or something along those lines . grad a: Prosody I don't really I 'm not really sure what prosody means , in this context , so I just made up you know whether whether what they say is or h how they say it is is that . grad a: the Parse would be what verb they chose , and then maybe how they modified it , in the sense of whether they said " I need to get there quickly " or whatever . grad a: And  , in terms of World Knowledge , this would just basically be like opening and closing times of things , the time of day it is , and whatnot . grad d: What 's " tourbook " ? grad a: Tourbook ? That would be , I don't know , the " landmark - iness " of things , grad d:   So let me see if I can ask grad a: Well , this is not a working Bayes - net . No , I understand that , but but  So , what Let 's slide back up again and see start at the at the bottom and Oop - bo - doop - boop - boop . So , you could imagine w  , go ahead , you were about to go up there and point to something . grad a:  I I 'd No , I was gonna wait until professor b: Oh , OK . So , so if you if we made if we wanted to make it into a a real  Bayes - net , that is , you know , with fill you know , actually f  , fill it @ @ in , then  grad a: So we 'd have to get rid of this and connect these things directly to the Mode . And and  Bhaskara and I was talking about this a little earlier today is , if we just do this , we could wind up with a huge  , combinatoric input to the Mode thing . And  grad a: Well I oh yeah , I unders I understand that , I just  it 's hard for me to imagine how he could get around that . Let me just mention something that I don't want to pursue today which is there are technical ways of doing it ,  I I slipped a paper to Bhaskara and about Noisy - OR 's and Noisy - MAXes and there 're ways to  sort of back off on the purity of your Bayes - net - edness . If you co you could ima and I now I don't know that any of those actually apply in this case , but there is some technology you could try to apply . grad a: So it 's possible that we could do something like a summary node of some sort that OK . grad a: So in that case , the sum we 'd have we  , these wouldn't be the summary nodes . We 'd have the summary nodes like where the things were I guess maybe if thi if things were related to business or some other professor b: Yeah . professor b: So what I was gonna say is is maybe a good at this point is to try to informally  , not necessarily in th in this meeting , but to try to informally think about what the decision variables are . So , if you have some bottom line  decision about which mode , you know , what are the most relevant things . professor b: And the other trick , which is not a technical trick , it 's kind of a knowledge engineering trick , is to make the n each node sufficiently narrow that you don't get this combinatorics . So that if you decided that you could characterize the decision as a trade - off between three factors , whatever they may be , OK ? then you could say " Aha , let 's have these three factors " , OK ? and maybe a binary version f for each , or some relatively compact decision node just above the final one . professor b: And then the question would be if if those are the things that you care about ,  can you make a relatively compact way of getting from the various inputs to the things you care about . So that y so that , you know , you can sort of try to do a knowledge engineering thing grad a: OK . professor b: given that we 're not gonna screw with the technology and just always use  sort of orthodox Bayes - nets , then we have a knowledge engineering little problem of how do we do that .  and grad a: So what I kind of need to do is to take this one and the old one and merge them together ? professor b: " Eh - eh - eh .  , so  , Robert has thought about this problem f for a long time , cuz he 's had these examples kicking around , so he may have some good intuition about you know , what are the crucial things . professor b: and , I understand where this the  this is a way of playing with this abs Source - path - goal trajector exp   abstraction and and sort of sh displaying it in a particular way . professor b: I don't think our friends  on Wednesday are going to be able to Well , maybe they will . This is sort of th the second version and I I I look at this maybe just as a , you know , a a whatever , UML diagram or , you know , as just a  screen shot , not really as a Bayes - net as John Johno said . grad a: We could actually , y yeah draw it in a different way , in the sense that it would make it more abstract . But the  the the nice thing is that you know , it just is a is a visual aid for thinking about these things which has comple clearly have to be specified m more carefully professor b: Alright , well , le let me think about this some more , grad d: and  professor b: and  see if we can find a way to present this to this linguists group that that is helpful to them . grad d: ultimately we we may w w we regard this as sort of an exercise in in thinking about the problem and maybe a first version of  a module , if you wanna call it that , that you can ask , that you can give input and it it 'll  throw the dice for you ,  throw the die for you , because  I integrated this into the existing SmartKom system in in the same way as much the same way we can  sort of have this  this thing . So if this is what M - three - L  will look like and what it 'll give us ,  And a very simple thing . We have an action that he wants to go from somewhere , which is some type of object , to someplace . grad d: And this these  this changed now only  ,  It 's doing it twice now because it already did it once .  , we 'll add some action type , which in this case is " Approach " and could be , you know , more refined  in many ways . grad d: Or we can  have something where the  goal is a public place and it will give us then of course an action type of the type " Enter " . So this is just based on this one  , on this one feature , and that 's that 's about all you can do . And so in the f if this pla if the object type  here is is a m is a landmark , of course it 'll be  " Vista " . And  this is about as much as we can do if we don't w if we want to avoid   a huge combinatorial explosion where we specify " OK , if it 's this and this but that is not the case " , and so forth , it just gets really really messy . You 're you 're grad d:  ? professor b: It was much too quick for me . So , I I do understand that  you can take the M - three - L and add not and it w and you need to do this , for sure , we have to add , you know , not too much about  object types and stuff , and what I think you did is add some rules of the style that are already there that say " If it 's of type " Landmark " , then you take you 're gonna take a picture of it . Ev - every landmark you take a picture of , grad d: Every public place you enter , and statue you want to go as near as possible . grad d: W professor b: that 's a that 's another kind of baseline case , that 's another sort of thing " OK , here 's a another kind of minimal  way of tackling this " . Add extra properties , a deterministic rule for every property you have an action , " pppt ! " You do that .  , then the question would be  Now , if that 's all you 're doing , then you can get the types from the ontology , OK ? because that 's all you 're all you 're using is this type the types in the ontology and you 're done . grad d:  ? professor b: Right ? So we don't we don't use the discourse , we don't use the context , we don't do any of those things . professor b: Alright , but that 's but that 's OK , and  it it 's again a kind of one minimal extension of the existing things . And that 's something the  SmartKom people themselves would they 'd say " Sure , that 's no problem you know , no problem to add types to the ont " Right ? grad d: Yeah . And this is just in order to exemplify what what we can do very , very easily is ,  we have this this silly  interface and we have the rules that are as banal as of we just saw , and we have our content . grad d: Now , the content I whi which is sort of what what we see here , which is sort of the Vista , Schema , Source , Path , Goal , whatever . grad d: This will  be  a job to find ways of writing down Image schema , X - schema , constructions , in some some form , and have this be in a in a in the content , loosely called " Constructicon " . And  and here is exactly where what 's gonna be replaced with our Bayes - net , which is exactly getting the input feeding into here . This decides whether it 's an whether action the the Enter , the Vista , or the whatever professor b: " approach " , you called it , I think this time . This is  so what we 'd be generating would be a reference to a semantic  like parameters for the for the X - schema ? professor b: For for for Yes . So that that  i if you had the generalized " Go " X - schema and you wanted to specialize it to these three ones , then you would have to supply the parameters . professor b: And then  , although we haven't worried about this yet , you might wanna worry about something that would go to the GIS and use that to actually get you know , detailed route planning . professor b: But , presumably that that that functionality 's there when when we grad a: So the immediate problem is just deciding w which grad d: Aspects of the X - schema to add . professor b: Yeah , so the pro The immediate problem is is back t t to what you were what you are doing with the belief - net . professor b: You know ,  what are we going to use to make this decision grad a: Right and then , once we 've made the decision , how do we put that into the content ? professor b: Yeah . professor b: The harder problem is we decide what we want to use , how are we gonna get it ? And that the the that 's the hardest problem . So , the hardest problem is how are you going to get this information from some combination of the what the person says and the context and the ontology . The h So , I think that 's the hardest problem at the moment is is grad a: OK .  , and that 's so , getting back to here , we have a d a technical problem with the belief - nets that we we don't want all the com grad a: There 's just too many factors right now . professor b: So we wanna think about which ones we really care about and what they really most depend on , and can we c you know , clean this this up to the point where it grad a: So what we really wanna do i cuz this is really just the three layer net , we wanna b make it expand it out into more layers basically ? professor b: Right .  , it 's true that the way you have this , a lot of the times you have what you 're having is the values rather than the variable . So instead of in instead it should really be just be " intention " as a node instead of " intention business " or " intention tour " . professor b: OK ? So you Yeah , right , and then it would have values , " Tour " , " Business " , or  " Hurried " . professor b: But then but i it still some knowledge design to do , about i how do you wanna break this up , what really matters . grad a: I think what was going through my mind when I did it was someone could both have a business intention and a touring intention and the probabilities of both of them happening at the same time professor b: Well , you you could do that . And it 's perfectly OK to  insist that that , you know , th  , they add up to one , but that there 's  that that it doesn't have to be one zero zero . So you could have the conditional p So the each of these things is gonna be a a a probability . So whenever there 's a choice ,  so like landmark - ness and usefulness , grad a: Well , see I don't think those would be mutually professor b: OK grad a: it seems like something could both be professor b: Absolutely right . professor b: And so that you might want to then have those b Th - Then they may have to be separate . professor b: So that 's but again , this is this is the sort of knowledge design you have to go through . It 's you know , it 's great is is , you know , as one step toward  toward where we wanna go . grad d: Also it strikes me that we we m may want to approach the point where we can sort of try to find a  , a specification for some interface , here that  takes the normal M - three - L , looks at it . Then we discussed in our pre - edu EDU meeting  how to ask the ontology , what to ask the ontology  the fact that we can pretend we have one , make a dummy until we get the real one , and so  we we may wanna decide we can do this from here , but we also could do it  you know if we have a a a belief - net interface . But this information is just M - three - L , and then we want to look up some more stuff in the ontology and we want to look up some more stuff in the maybe we want to ask the real world , maybe you want to look something up in the GRS , but also we definitely want to look up in the dialogue history  some s some stuff . Based on we we have  I was just made some examples from the ontology and so we have for example some information there that the town hall is both a a a building and it has doors and stuff like this , but it is also an institution , so it has a mayor and so forth and so forth and we get relations out of it and once we have them , we can use that information to look in the dialogue history , " were any of these things that that are part of the town hall as an institution mentioned ? " , professor b:   grad d: " were any of these that make the town hall a building mentioned ? " , grad c: Right . So this may be a a sort of a process of two to three steps before we get our vector , that we feed into the belief - net , professor b: Yeah . grad d: and then professor b: There will be rules , but they aren't rules that come to final decisions , they 're rules that gather information for a decision process . So they 'll they presumably there 'll be a thread or process or something that " Agent " , yeah , " Agent " , whatever you wan wanna say , yeah , that  is rule - driven , and can can  can do things like that . And  there 's an issue about whether there will be that 'll be the same agent and the one that then goes off and  carries out the decision , so it probably will . My guess is it 'll be the same basic agent that  can go off and get information , run it through a a c this belief - net that turn a crank in the belief - net , that 'll come out with s  more another vector , OK , which can then be  applied at what we would call the simulation or action end . So on once you pull that out , it could be that that says " Ah ! Now that we know that we gonna go ask the ontology something else . " OK ? Now that we know that it 's a bus trip , OK ? we didn't We didn't need to know beforehand ,  how long the bus trip takes or whatever , but but now that we know that 's the way it 's coming out then we gotta go find out more . So this is actually , s if if we were to build something that is  , and , I had one more thing , the it needs to do Yeah . I think we I I can come up with a a code for a module that we call the " cognitive dispatcher " , which does nothing , professor b: OK . grad d: but it looks of complect object trees and decides how are there parts missing that need to be filled out , there 's this is maybe something that this module can do , something that this module can do and then collect  sub - objects and then recombine them and put them together . So maybe this is actually some some useful tool that we can use to rewrite it , and  get this part , professor b: Oh , OK . In particular see what we 'd like to do , and and this has been implicit in the discussion , is to do this in such a way that you get a lot of re - use . What you 're trying to get out of this deep co cognitive linguistics is the fact that w if you know about source source , paths and goals , and nnn all this sort of stuff , that a lot of this is the same , for different tasks . And that  there 's there 's some some important generalities that you 're getting , so that you don't take each and every one of these tasks and hafta re - do it . grad d: There 're no primitives upon which  professor b: u u What are the primitives , and how do you break this grad d: yeah . professor b: So I y I 'm just just there saying eee well you I know how to do any individual case , right ? but I don't yet see what 's the really interesting question is can you use  deep  cognitive linguistics to get powerful generalizations . professor b:  grad d: Maybe we sho should we a add then the " what 's this ? " domain ? N  , we have to " how do I get to X " . Then we also have the " what 's this ? " domain , where we get some slightly different professor b: Could . grad d:  Johno , actually , does not allow us to call them " intentions " anymore . professor b: Well , I I don't like the term either , so I have n i  i i y w i i It  grad d: But  , I 'm sure the " what 's this ? " questions also create some interesting X - schema aspects . I 'm not a I 'm not op particularly opposed to adding that or any other task , grad d: So . professor b: I 'm just saying that I 'm gonna hafta do some sort of first principles thinking about this . Well , no the Bayes the Bayes - nets The Bayes - nets will be dec specific for each decision . But what I 'd like to be able to do is to have the way that you extract properties , that will go into different Bayes - nets , be the  general . So that if you have sources , you have trajectors and stuff like that , and there 's a language for talking about trajectors , you shouldn't have to do that differently for   going to something , than for circling it , for  telling someone else how to go there , grad d: Getting out of professor b: whatever it is . So that that , the the decision processes are gonna be different What you 'd really like of course is the same thing you 'd always like which is that you have  a kind of intermediate representation which looks the same o over a bunch of inputs and a bunch of outputs . So all sorts of different tasks and all sorts of different ways of expressing them use a lot of the same mechanism for pulling out what are the fundamental things going on . And pushing it one step further , when you get to construction grammar and stuff , what you 'd like to be able to do is say you have this parser which is much fancier than the parser that comes with  SmartKom , i that that actually uses constructions and is able to tell from this construction that there 's  something about the intent you know , the actual what people wanna do or what they 're referring to and stuff , in independent of whether it about what is this or where is it or something , that you could tell from the construction , you could pull out deep semantic information which you 're gonna use in a general way . You might be able to to  say that this i this is the kind of construction in which the there 's Let 's say there 's a  cont there the the land the construction implies the there 's a con this thing is being viewed as a container . So just from this local construction you know that you 're gonna hafta treat it as a container you might as well go off and get that information . So if you say " how do I get into the castle " OK , then  Or , you know , " what is there in the castle " or so there 's all sorts of things you might ask that involve the castle as a container and you 'd like to have this orthogonal so that anytime the castle 's referred to as a container , you crank up the appropriate stuff . professor b: Alright , so that 's that 's the that 's the thesis level grad d:   professor b:  grad d: It 's unfortunate also that English has sort of got rid of most of its spatial adverbs because they 're really fancy then , in in for these kinds of analysis . professor b: Well , you have prepositional phrases that grad d: Yeah , but they 're they 're easier for parsers . grad d: Parsers can pick those up but but the with the spatial adverbs , they have a tough time . Oh yeah , b But an architecture like this would also enable us maybe to to throw this away and and replace it with something else , or whatever , so that we have so that this is sort of the representational formats we 're we 're we 're talking about that are independent of the problem , that generalize over those problems , and are oh , t of a higher quality than an any actual whatever  belief - net , or " X " that we may use for the decision making , ultimately . So , are we gonna be meeting here from now on ? I 'm I 'm happy to do that . We we had talked about it , cuz you have th th the display and everything , that seems fine . grad d: so far I think it was nice as a visual aid for some things and and professor b: Oh yeah . No I I think it 's worth it to ass to meet here to bring this , and assume that something may come up that we wanna look at . grad d: Yeah ? The   , she w she was definitely good in the sense that she she showed us some of the weaknesses professor b: Right . grad d: and  also the  the fact that she was a real subject you know , is is professor b: Right . Yeah , and and and yeah and and she took it seriously and stuff l No , it was great . grad d: So I think that   , w Looking just looking at this data , listening to it , what can we get out of it in terms of our problem , for example , is , you know , she actually m said you know , she never s just spoke about entering , she just wanted to get someplace , and she said for buying stuff . grad d: and in the other case , where she wanted to look at the stuff at the graffiti , also , of course , not in the sentence " How do you get there ? " was pretty standard . Nuh ? except that there was a nice anaphora , you know , for pointing at what she talked about before , and there she was talking about looking at pictures that are painted inside a wall on walls , so grad c: Right .  , because graffiti is usually found on the outside and not on the inside , grad c: Yeah