grad c: Now can you give me the  remote T ? professor d: OK , so Eva , co  could you read your numbers ? grad a: Go ahead and read . grad b: Robert , do you  have any way to turn off your  screensaver on there so that it 's not going off every  , it seems to have about at two minute grad c: Yeah , I 've I  it 's not that I didn't try . grad c: and  I I told it to stay on forever and ever , but if it 's not plugged in it just doesn't obey my commands . grad c: But  we 'll just be m m working on it at intensity so it doesn't happen . grad c: So what I 've tried to do here is list all the decision nodes that we have identified on this side . So all of these tasks The reading is out of these tasks more or less imply that the user wants to go there , sometime or the other . And these are the data tasks where w we can assume the person would like to enter , view or just approach the thing . Analogously the same on the object information we can see that , you know , we have sort of created these tasks before we came up with our decision nodes so there 's a lot of things where we have no analogous tasks , and that may or may not be a problem . We can change the tasks slightly if we feel that we should have data for e sort of for every decision node so trying to im  implant the intention of going to a place now , going to a place later on the same tour , or trying to plant the intention of going sometime on the next tour , or the next day or whenever .  one of them is to work on the semantics of the belief - net which is going to be the main inference engine for thi the system  making decisions . so f the natural language understanding thing is  , we think gonna only have to choose parameters , but You know , a fairly large set of parameters . One of which is figure out what all the choices are , which we 've done a fair amount . Then we need to figure out what influences its choices and finally we have to do some technical work on the actual belief relations and presumably estimates of the probabilities and stuff . And we 're also , sort of  in the same process , going to work with Fey on what there should be in the dialogues . So One of the s steps that 's coming up real soon is to actually get subjects  in here , and have them actually record like this . And depending on what Fey sort of provokes them to say , we 'll get information on different things . grad c: Well how people phrase different intentions more or less , professor d: So Fo - v yeah people with the phrase them grad c: huh ? professor d: and so  for , you know , Keith and people worrying about what constructions people use ,  we have some i we have some ways to affect that by the way the dialogues go . So what Robert kindly did , is to lay out a table of the kinds of  things that that might come up , and , the kinds of decisions . So if if we 're right , you can get by with  just this middle column worth of decisions , and it 's not all that many , and it 's perfectly feasible technically to build belief - nets that will do that . grad c:  you can look at this if you want , these are what our subject 's going to have to fill out . grad c: Let me summarize in two sentences , mainly for Eva 's benefit , who probably has not heard about the data collection , at all . grad c: Or have you heard about it ? grad a: Not that much you didn't . grad c: Then then they will read  a task where lots of German words are sort of thrown in between . And  and they have to read isolated proper names And these change professor d: S I don't see a release grad c: No , this is not the release form . grad c: And  And then they gonna have to f   choose from one of these tasks , which are listed here . Six different things they sort of think they would do if they were in Heidelberg or traveling someplace and  and they have a map . grad c: And  grad b: This is a fictional system obviously , grad c: The comp Yeah , the computer system sits right in front of you , grad b: huh . So she can copy sentences into a window , or type really fast and this machine will use speech synthesis to produce that . So if you ask " How do I get to the castle " then a m s several seconds later it 'll come out of here " In order to get to the castle you do " grad b: Yeah . " And we sort of get the idea what people do when they s think they speak to a machine and what people say when they think they speak to a human , or know , or assume they speak to a human . grad c: And we 're gonna start tomorrow at three ? four ? one ? undergrad e: Tomorrow , well we don't know for sure . undergrad e: but grad c: And  we 're still l looking for a room on the sixth floor because they stole away that conference room . But professor d: Well , there are these   oh , I see , we have to Yeah , it 's tricky . And  I can report a so I did this but I also tried to do this so if I click on here , Isn't this wonderful ? we get to the   belief - net just focusing on on the g Go - there node . grad c: And what w what happened is that  design - wise I 'd sort of n noticed that we can we still get a lot of errors from a lot of points to one of these sub Go - there User Go - there Situation nodes . So I came up with a couple of additional nodes here where  whether the user is thrifty or not , and what his budget is currently like , is going to result in some financial state of the user . Being the same at this just the money available , which may influence us , whether he wants to go there if it is you know charging tons of dollars for admission or its gonna g cost a lot of t e whatever . grad c: So , and this actually turned out to be pretty key , because having specified sort of these  this this intermediate level  and sort of noticing that everything that happens here let 's go to our favorite endpoint one is again more or less we have  then the situation nodes contributing to the the endpoint situation node , which contributes to the endpoint and so forth .  I can now sort of draw straight lines from these to here , meaning it g of course goes where the sub - S everything that comes from situation , everything that comes from user goes with the sub - U , and whatever we specify for the so - called " Keith node " , or the discourse , what comes from the  parser , construction parser ,  will contribute to the D and the ontology to the sub - O node . And  one just s sort of has to watch which also final decision node so it doesn't make sense t to figure out whether he wants to enter , view or approach an object if he never wants to go there in the first place . And  now all w that 's left to do then is the CPG 's , the conditional probabilities , for the likelihood of a person having enough money , actually wanting to go a place if it costs , you know this or that . and once  Bhaskara has finished his classwork that 's where we 're gonna end up doing . And  And for now  the the question is " How much of these decisions do we want to build in explicitly into our data collection ? " So  , one could sort of think of you know we could call the z see or you know , people who visit the zoo we could s call it " Visit the zoo tomorrow " , so we have an intention of seeing something , but not now but later . Yeah , so let 's s  s see I th I think that from one point of view , all these places are the same , so that d d That ,  in terms of the linguistics and stuff , there may be a few different kinds of places , so I th i it seems to me that We ought to decide you know , what things are k are actually going to matter to us .  are all big - ish things that  you know have different parts to them , and one of them might be fine . Yeah The the reason why we did it that way , as a as a reminder , is  no person is gonna do all of them . professor d: And grad c: They 're just gonna select u  , according to their preferences . grad c: " Ah , yeah , I usually visit zoos , or I usually visit castles , or I usually " And then you pick that one . professor d: Right , no no , but but s th point is to to y to build a system that 's got everything in it that might happen you do one thing . professor d: T to build a system that  had the most data on a relatively confined set of things , you do something else . So , all I 'm saying is i th there 's there 's a kind of question of what we 're trying t to accomplish . and I think my temptation for the data gathering would be to  , you know And each person is only gonna do it once , so you don't have to worry about them being bored , so if if it 's one service , one luxury item , you know , one big - ish place , and so forth and so on ,  then my guess is that that the data is going to be easier to handle . Now of course you have this I guess possible danger that somehow there 're certain constructions that people use  when talking about a museum that they wouldn't talk about with a university and stuff ,  but I guess I 'm I  m my temptation is to go for simpler . But I don't know what other people think about this in terms of grad b: So I don't exactly understand professor d:  grad b: like I I I guess we 're trying to limit the detail of our ontology or types of places that someone could go , right ? But who is it that has to care about this , or what component of the system ? professor d: Oh , well ,  th I think there are two places where it comes up . One is  in the th these people who are gonna take this and and try to do speech with it . professor d:  Lots of pronunciations of th of the same thing are going to give you better data than l you know , a few pronunciations of lots more things . grad b: So we would rather just ask  have a bunch of people talk about the zoo ,  and assume that that will that the constructions that they use there will give us everything we need to know about these sort of zoo , castle , whatever type things , these bigger places . professor d: Bigger Y yeah thi well this is a question for grad b: And that way you get the speech data of people saying " zoo " over and over again or whatever too . professor d: and , you know , if we if we do , and we probably will , actually try to  build a prototype ,  probably we could get by with the prototype only handling a few of them anyway . So ,  grad c: Yeah , the this was sort of these are all different sort of activities . So , " Go to place " and then give them a choice , you know either they 're the symphony type or opera type or the tourist site guide type or the nightclub disco type person and they say " yeah this is on that " go to big - ish place " , grad b:   " And then we have the " Fix " thing , and then maybe " Do something the other day " thing , so . My question is I guess , to some extent , we should y we just have to try it out and see if it works . It would be challenging , in in a sense , to try to make it so so complex that they even really should schedule , or to plan it , a more complex thing in terms of OK , you know , they should get the feeling that there are these s six things they have to do and they sh can be done maybe in two days . grad c: So they make these decisions , professor d: Well I think th th grad c: " Can I go there tomorrow ? " professor d: yeah . So , the  system could say , " Well ,  we 'd like to to set up your program for two days in Heidelberg , you know , let 's first think about all the things you might like to do . So there th i i in  in I th I I 'm sure that if that 's what you did then they would start telling you about that , and then you could get into  various things about ordering , if you wanted . And that can be done , sort of to say , " OK now we 've picked these six tasks . grad c: And th w professor d: No , we have to help we have to decide .  , one is a planner The other , it kind of give you instructions on the spot grad c: Yeah , but th the I don't I 'm not really interested in sort of " Phase planning " capabilities . But it 's more the how do people phrase these planning requests ? So are we gonna masquerade the system as this as you said simple response system , " I have one question I get one response " , or should we allow for a certain level of complexity . professor d: Well , so Keith , what do you think ? grad b: Well ,  it seems that Yeah , off the top of my head it kinda seems like you would probably just want , you know , richer data , more complex stuff going on , people trying to do more complex sets of things .  you know , if our goal is to really sort of be able to handle a whole bunch of different stuff , then throwing harder situations at people will get them to do more linguistic more interesting linguistic stuff . But  I 'm I 'm not really sure  , because I don't fully understand like what our choices are of ways to do this here yet . grad c:  w we have tested this and a y have you heard listen to the f first two or th as a matter of fact the second person is  is was faced with exactly this kind of setup . grad b: I started to listen to one and it was just like , sort of depressing . grad c: And grad b: I thought I 'd just sort of listen to the beginning part and the person was just sort of reading off her script or something . grad b: So  , I grad c: it is already with this it got pretty with this setup and that particular subject it got pretty complex . grad c: Maybe I suggest we make some fine tuning of these , get sort of run through ten or so subjects grad b:   grad c: and then take a breather , and see whether we wanna make it more complex or not , depending on what what sort of results we 're getting . It In fact , I am just you know today , next couple days gonna start really diving into this data . I 've basically looked at one of the files you know one of these l y y y you gave me those dozens of files and I looked at one of them which was about ten sentences , found fifteen , twenty different construction types that we would have to look for and so on and like , " alright , well , let 's start here . So I haven't really gone into the , you know looked at all of the stuff that 's going on . So I don't really Right , once I start doing that I 'll have more to say about this kind of thing . grad c: And y and always professor d: But well th but you did say something important , which is that  you can probably keep yourself fairly well occupied  with the simple cases for quite a while . professor d: Although , obviously th so so that sa s does suggest that  , now , I have looked at all the data , and it 's pre it 's actually at least to an amateur , quite redundant . professor d: That that it was it was very stylized , and quite a lot of people said more or less the same thing . grad b: I  I did sort of scan it at first and noticed that , and then looked in detail at one of them . So grad c:  w do we wanna get going beyond more , which is sort of the professor d: Well , OK , so let 's let 's take let 's I I think your suggestion is good , which is we 'll do a b  a batch . And , Fey , How long is it gonna be till you have ten subjects ? Couple days ? Or thr f a A week ? Or I don't I don't have a feel for th undergrad e:  I can Yeah ,  I s I think can probably schedule ten people , whenever . professor d: Well , it 's it 's up to you ,  I j I  e We don't have any huge time pressure . It 's just when you have t undergrad e: How long will it be ? professor d: Yeah . grad c: This means audio , but professor d: and Huh ? grad c: no transcriptions of course , yeah . professor d: No , we won't have the transcriptions , but what we should be able to do and I don't know if , Fey , if you will have time to do this , but it would be great if you could , not transcribe it all , but pick out  , some stuff . professor d: Oh , we could get  , you can buy another disk for two hundred dollars , right ?  it 's it 's not like OK . grad c: No , he  , he he has been solving all our problems or is wonderful , professor d: OK . But anyway , so , If you if you can think of a way to  , point us to th to interesting things , sort of as you 're doing this or or something  , make your make notes or something that that this is , you know , something worth looking at . And other than that , yeah I guess we 'll just have to  , listen although I guess it 's only ten minutes each , right ? Roughly . professor d: People I understand , but people people you know  undergrad e: It feels like a long time grad c: Yeah .  But anyway yeah , so I think it 's a good idea to start with the sort of relatively straight forward res just response system . And then if we want to  get them to start doing  multiple step planning with a whole bunch of things and then organize them an  tell them which things are near each other and you know , any of that stuff .  You know , " Which things would you like to do Tuesday morning ? " grad c: Yeah . undergrad e: But were you saying that grad c: I need those back by the way . grad c: That 's for professor d: I 'm sorry , Fey , what ? undergrad e: That w maybe one thing we should do is go through this list and sort of select things that are categories and then o offer only one member of that category ? professor d: That 's what I was suggesting for the first round , yeah . undergrad e: And then , they could be alternate versions of the same If you wanted data on different constructions . professor d: They could , but i but i  tha eh they c yeah , but  but undergrad e: Like one person gets the version with the zoo as a choice , and the other person gets the professor d: You could , but i but I I I think in the short run , grad c: And no , th the per the person don't get it .  , this is why we did it , because when we gave them just three tasks for w part - A and three tasks for part - B a professor d: Right . But  I I think this approach will very well work , but the person was able to look at it and say " OK , This is what I would actually do . grad c: OK , we gotta we gotta disallow  traveling to zoos and  castles at the same time , sort of undergrad e:  there they are significantly different , but . grad c: But no , they 're  they 're sort of this is where tour becomes you know tourists maybe a bit different undergrad e: Yeah , I guess so . grad c: But we can  professor d: Yeah , in fact if y if y if you use the right verb for each in common , like at you know , " attend a theater , symphony or opera " is is a group , and " tour the university , castle or zoo " , grad c:  -  Yeah . professor d: all of these d do have this kind of " tour "  aspect about the way you would go to them . professor d: So it may turn out to be not so many different kinds of things , grad c:   professor d: and then , what one would expect is that that the sentence types would  their responses would tend to be grouped according to the kind of activity , you would expect . phd f: But  i it seem that  there is a difference between going to see something , and things like " exchange money " or " dine out " professor d: Oh , absolutely . grad c: Yeah , this is where yeah th the function stuff is definitely different and the getting information or g stuff yeah . So since people gonna still pick something , we we 're not gonna get any significant amount of redundancy . And  we would be ultimately more interested in getting all the possible ways of people asking , oh , for different things with or with a computer . And so if you can think of any other sort of high level tasks a tourist may do just always just m mail them to us and we 'll sneak them into the collection . But it seems like since we since we are getting towards  subject  fifty subjects and if we can keep it up  to a  sort of five four - ish per week rate , we may even reach the one hundred before Fey t takes off to Chicago . professor d: Well , these are all f people off campus s from campus so far , undergrad e: Yeah . professor d: So we yeah we don't know how many we can get next door at the  shelter for example . So , alright , so let 's go let 's go back then , to the the chart with all the decisions and stuff , and see how we 're doing . professor d: Do do people think that , you know this is is gonna  cover what we need , or should we be thinking about more ? grad c: Okay , in terms of decision nodes ?  , Go - there is is a yes or no . grad c: I 'm also interested in th in this " property "  line here , so if you look at sorry , look at that  , timing was  I have these three . Do we need a final differentiation there ? Now , later on the same tour , sometimes on the next tour . grad b: What 's this idea of " next tour " ?  grad c: It 's sort of next day , so you 're doing something now and you have planned to do these three four things , grad b:   grad c: you could sort of tag it on to that tour grad b: Or OK . grad c: or you can say this is something I would do s I wanna do sometime l in my life , basically . So so this tour is sort of just like th the idea of current s round of of touristness or whatever , professor d: Right . professor d:  if you if if you wanted precise about it ,  you know , grad b: Got it . professor d: You know , " OK , we 'll go back to the hotel and then we 'll go off grad b: OK . professor d: and " phd f: So all tours b a tour happens only within one day ? professor d: Yes . grad c: Well , my visit to Prague there were some nights where I never went back to the hotel , so whether that counts as a two - day tour or not we 'll have to think . grad b: You just spend the whole time at U Fleku or something , phd f: Yeah . What is the  the the English co   cognate if you want , for " Sankt Nimmerlandstag " ? grad b: Keine Ahnung grad c: Sort of " We 'll do it on when you say on that d day it means it 'll never happen . grad c: Do you have an expression ? Probably you sh grad b: Not that I know of actually . grad c: Yeah , when hell Yep , we 'll do it when hell freezes over .  , the reason why why do we go there in the first place IE  it 's either  for sightseeing , for meeting people , for running errands , or doing business . grad b: So , business is supposed to  , be sort of it like professional type stuff , right , or something like that ? grad c: Yep . " Who is the the tour is the person ? " So it might be a tourist , grad b:   grad c: it might be a business man who 's using the system , who wants to sort of go to some grad b: Yeah . He is going to  He 's there to teach a course at the business school but he also is touring around and so he may have some mixture of these things . grad c: He would phd f: What ab What do you have in mind in terms of  socializing ? What kind of activities ? grad c: Eh , just meeting people , basically . " I want to meet someone somewhere " , which be puts a very heavy constraint on the " EVA " phd f: Oh grad b: Yeah . grad c: you know , because then if you 're meeting somebody at the town hall , you 're not entering it usually , you 're just want to approach it . grad b: So  , does this capture , like , where do you put " Exchange money " is an errand , right ? But what about  grad c: Yep . professor d:  -  grad b: So , like " Go to a movie " is now entertainment , " Dine out " is phd f: Socializing , I guess . professor d: but but  I would say that if " Dine out " is a special c  if you 're doing it for that purpose then it 's entertainment . professor d: And we 'll also as y as you 'll s further along we 'll get into business about " Well , you 're you know this is going over a meal time , do you wanna stop for a meal or pick up food or something ? " grad b:   That 's that 's sort of part of th that 's not a destination reason , that 's sort of " en passant , " right .  , " mode " , I have found three , " drive there " , " walk there "  or " be driven " , which means bus , taxi , BART . professor d: Obviously taxis are very different than buses , but on the other hand the system doesn't have any public transport This the planner system doesn't have any public transport in it yet . grad c: So this granularity would suffice , I think w if we say the person probably , based on the utterance we on the situation we can conclude wants to drive there , walk there , or use some other form of transportation . grad b: H How much of Heidelberg can you get around by public transport ?  in terms of the interesting bits . There 's lots of bits where you don't really I 've only ev was there ten years ago , for a day , so I don't remember , but .  , like the sort of the tourist - y bits professor d:  - Well , grad c: Everywhere . grad b: is it like professor d: you can't get to the Philosophers ' Way very well , grad b: Yeah . professor d: but ,  there are hikes that you can't get to , but grad b: OK . grad a: So is like " biking there " part of like " driving there " , grad c: Yeah ,  we actually biking should be should be a separate point because we have a very strong bicycle planning component . grad c: Bicycles c should be in there , but , will we have bic  is this realistic ?  grad b: Yeah . grad c: We can we can sort of  , drive grad b: I would I would lump it with " walk " because hills matter . professor d: right ? grad c: OK , " Length " is  , you wanna get this over with as fast as possible , professor d: Alright . But we should just make a decision whether we feel that they want to use some substantial or some fraction of their time . grad c: You know , they wanna do it so badly that they are willing to spend  you know the necessary and plus time . And  And y you know , if we feel that they wanna do nothing but that thing then , you know , we should point out that to the planner , that they probably want to use all the time they have . It seems like it  you 're you 're talking about rather than having the user decide this you 're supposed t we 're supposed to figure it out ? professor d: w well grad c: Th - the user can always s say it , but it 's just sort of we we hand over these parameters if we make if we have a feeling that they are important . grad c: And that we can actually infer them to a significant de degree , or we ask . professor d: And par yeah , and part of the system design is that if it looks to be important and you can't figure it out , then you ask . professor d: But hopefully you don't ask you know , a all these things all the time . professor d: Or eh so , y but there 's th but definitely a back - off position to asking . grad c: And if no no part of the system ever comes up with the idea that this could be important , no planner is ever gonna ask for it . grad c: y so And I like the idea that , you know , sort of Jerry pushed this idea from the very beginning , that it 's part of the understanding business to sort of make a good question of what 's s sort of important in this general picture , what you need t grad b:   grad c: If you wanna simulate it , for example , what parameters would you need for the simulation ? And , Timing , Length would definitely be part of it , " Costs " , " Little money , some money , lots of money " ? professor d:   But a I as a tourist , I 'll just paying what 's what 's more or less is required . I think there are there 're different things where you have a ch choice , undergrad e: Mmm . professor d: for example ,  this t interacts with " do am I do oh are you willing to take a taxi ? " grad b: Dinner . professor d: Or  , you know , if if you 're going to the opera are you gonna l look for the best seats or the peanut gallery phd f: The best seat or or Right . So professor d: whatever ? S so I think there are a variety of things in which  Tour - tourists really do have different styles eating . grad c: The what what my sort of sentiment is they 're Well , I I once had to write a a a a charter , a carter for a a student organization . And I looked at the other ones and they always said ten percent of the student body has to be present at their general meeting otherwise it 's not a And I wrote in there " En - Enough " people have to be there . And it was hotly debated , but people agreed with me that everybody probably has a good feeling whether it was a farce , a joke , or whether there were enough people . grad c: And if you go to Turkey , you will find when people go shopping , they will say " How much cheese do you want ? " and they say " Ah , enough . Because the person selling the cheese knows , you know , that person has two kids and you know , a husband that dislikes cheese , so this is enough . grad c: And  so the middle part is always sort of the the golden way , right ? So you can s you can be really make it as cheap as possible , or you can say " I want , er , you know , I don't care " grad b: Money is no object . grad c: in much the same way as how how d you know should the route be ? Should it be the easiest route , even if it 's a b little bit longer ? grad b:   grad c: No steep inclinations ? Go the normal way ? Whatever that again means , er or do you does the person wanna rough it ? grad b:    th so there 's a couple of different ways you can interpret these things right ? You know " I want to go there and I don't care if it 's really hard . grad b: Right ? you know , so I assume we 're going for the first interpretation , undergrad e: Right . grad b: right ? Something like I 'll go th  I 'd li I dunno . It 's different from thing to professor d: No , I think he was going for the second one ar actually . grad b: Yeah ? I I professor d: Anyway , we 'll sort th yeah , we 'll sort that out .  " Object information " , " Do I do I wanna know anything about that object ? " is either true or false . if I care about it being open , accessible or not , I don't think there 's any middle ground there .  , either I wanna know where it is or not , I wanna know about it 's history or not , or ,  I wanna know about what it 's good for or not . professor d: Yeah , now ob OK , I 'm sorry , go ahead , what were you gonna say ? grad c: One could put scales in there . So " object " becomes " entity " , right ? grad c: Yep , that 's true . professor d: And we think that 's it , interestingly enough , that  , you know , th or or or something very close to it is going to be  going to be enough . professor d: Alright , so  So I think the order of things is that  , Robert will clean this up a little bit , although it looks pretty good . And grad c: What , well this is the part that professor d: Huh ? grad c: this is the part that needs the work . professor d: Yeah , so right , so So ,  In parallel ,  three things are going to happen .  Robert and Eva and Bhaskara are gonna actually build a belief - net that that , has CPT 's and , you know , tries to infer this from various kinds of information . And Fey is going to start collecting data , and we 're gonna start thinking a about  what constructions we want to elicit . And then w go it may iterate on  , further data collection to elicit grad b: D Do you mean Do you mean eliciting particular constructions ? Or do you mean like what kinds of things we want to get people talking about ? Semantically speaking , eh ? professor d: Well , yes . professor d: And And so  grad b:  from my point of view I 'm I 'm trying to care about the syntax , so you know professor d: Well that too , grad b: OK . professor d: but  You know if th if we in if we you know , make sure that we get them talking about temporal order . professor d: OK , that would be great and if th if they use prepositional phrases or subordinate clauses or whatever , grad b:   professor d: But I I think that probably we 're gonna try to look at it as you know , s what semantic constructions d do we do we want them to  do direc grad b: OK . professor d: you know , " Caused motion " , I don't know , something like that . professor d:  But , Eh -  this is actually a conversation you and I have to have about your thesis fantasies , and how all this fits into that . grad c: Because I have not been able to dig out all the stuff out of the m ta thirty D V grad b: OK . grad c:  If you grad b: Is that roughly the equivalent of of what I 've seen in English or is it grad c: No , not at all . Like what What have I got now ?  I have  what what I 'm loo what I Those files that you sent me are the user side of some interaction with Fey ? grad c: A little bit of data , I grad b: Is that what it is ? Or ? grad c: With nothing . grad c: And  the see this this this  ontology node is probably something that I will try to expand . Once we have the full ontology API , what can we expect to get from the ontology ? And hopefully you can sort of also try to find out , you know , sooner or later in the course of the summer what we can expect to get from the discourse that might , you know or the grad b:   grad c: in terms of  professor d: Right , but we 're not expecting Keith to actually build a parser .  He 's g he 's hoping to do this for his masters ' thesis s by a year from now . grad b:  sometime , I have to talk to some subset of the people in this group , at least about  what sort of constructions I 'm looking for .  , you know obviously like just again , looking at this one  thing , you know , I saw y things from sort of as general as argument structure constructions . I have to do   unbounded dependencies , you know , which have a variety of constructions in   instantiate that . On the other hand I have to have , you know , there 's particular  , fixed expressions , or semi - fixed expressions like " Get " plus path expression for , you know , " how d ho how do I get there ? " , professor d:   grad b: " How do I get in ? " , " How do I get away ? " professor d: Right . Like professor d: OK , so this is I think we 're gonna mainly work on with George . professor d: OK , and hi let me f th say what I think is is so the idea is  first of all I misspoke when I said we thought you should do the constructions . So what I yeah , OK , So what what I meant was " Do a first cut at " . professor d: OK , Because  we do wanna get them r u perfectly but I think we 're gonna have to do a first cut at a lot of them to see how they interact . And I I me it would it would be completely out of the question to really do more than , say , like , oh I don't know , ten , over the summer , professor d: Yeah . grad b: but  , but you know obviously we need to get sort of a general view of what things look like , so yeah . So the idea is going to be to do sort of like Nancy did in some of the er these papers where you do enough of them so you can go from top to bottom so you can do f you know , f f  have a complete story ov of s of some piece of dialogue . professor d: And that 's gonna be much more useful than having all of the clausal constructions and nothing else , or or or something like that . professor d: So that the the trick is going to be t to take this and pick a some sort of lattice of constructions , grad b:   professor d: so some lexical and some phrasal , and and , you know , grad b:   professor d: whatever you need in order to  , be able to then , by hand , you know , explain , some fraction of the utterances . professor d: And so , exactly which ones will partly depend on your research interests and a bunch of other things . But  in terms of the s th sort of level of  of analysis , you know , these don't necessarily have to be more complex than like the " Out of " construction in the BCP paper where it 's just like , you know , half a page on each one or something . professor d: And if if there 's something that really requires a lot more than that then it does and we have to do it , grad b: Yeah . grad c: We could sit down and think of sort of the the ideal speaker utterances , grad b:   grad c: and  two or three that follow each other , so , where we can also sort of , once we have everything up and running , show the tremendous , insane inferencing capabilities of our system . This is sort of their standard demo dialogue , which is , you know , what the system survives and nothing but that . grad c: we could also sor sort of have the analogen of o our sample sentences , the ideal sentences where we have complete construction coverage and , sort of , they match nicely . grad c: So the the " How do I get to X ? " , grad b: Yeah . grad c: And hey , that 's that 's already covering eighty percent of the system 's functionality . grad c: No , we can w throw in an " Out of Film " construction if you want to , but professor d: No , no , no . Well the th the thing is there 's a lot that needs to be done to get this right . professor d: OK , I th We done ? grad c: I have one bit of news . grad c: the action planner guy has wrote has written a a p lengthy proposal on how he wants to do the action planning . grad c: And I responded to him , also rather lengthy , how he should do the action planning . And I tacked on a little paragraph about the fact that the whole world calls that module a dis disc dialogue manager , professor d: Right . grad c: and wouldn't it make sense to do this here too ? professor d: Right . grad c: And also Rainer M Malaka is going to be visiting us shortly , most likely in the beginning of June . grad c: And  m making me incapable of going to NAACL , for which I had funding . grad c: When is the  Santa Barbara ? professor d: S grad c: Who is going to ?  should a lot of people . grad b: How much does it cost ? grad c: There 's grad b: I haven't planned to go . So , if we all decide it 's a good idea for you to go then you 'll we 'll pay for it . professor d:  I I don't have a feeling one way or the other at the moment , grad b: OK