input: Grad C: Nice .
  Grad D: OK .
  Grad A: to {disfmarker} to handle .
  Grad D: Is that good ?
  Grad C: Right . Yeah , I 've have never handled them .
  Grad B: Goats eat cans , to my understanding . Tin cans .
  Grad D: Did we need to do these things ?
  Grad C: Wow .
  Grad D: OK .
  Grad B: Could I hit {disfmarker} hit F - seven to do that ? on the {disfmarker} Robert ?
  Grad A: I 'm
  Grad B: Oh , the remote will do it OK .
  Grad D: OK .
  Grad B: Cuz I 'm already up there ?
  Grad A: in control here .
  Grad B: You are in control . Already ?
  Grad D: Wow , we 're all so high tech here . Yet another p PowerPoint presentation .
  Grad B: I {disfmarker} Well it makes it easier {pause} to do
  Grad D: Certainly does .
  Grad B: So , {pause} we were {disfmarker} Ah !
  Grad C: Johno , where are you ?
  Grad B: OK . So , Let 's see . Which one of these buttons will do this for me ? Aha ! OK .
  Grad C: Should you go back to the first one ?
  Grad B: Do I wanna go back to the first one ?
  Grad C: Well {disfmarker}
  Grad B: OK .
  Grad D: I 'm sorry I {disfmarker}
  Grad C: Well , I mean , {pause} just to {disfmarker}
  Grad B: OK . Introduce .
  Grad D: OK .
  Grad C: Yeah , um {vocalsound} Well , " the search for the middle layer " . It 's basically uh talks about uh {disfmarker} {vocalsound} It just refers to the fact that uh {pause} one of main things we had to do was to {pause} decide what the intermediate sort of nodes were ,
  Grad D: I can read ! I 'm kidding .
  Grad C: you know , because {disfmarker}
  Grad D: Mm - hmm .
  Grad A: But if you really want to find out what it 's about you have to {pause} click on the little {pause} light bulb .
  Grad B: Although I 've {disfmarker} I 've never {disfmarker} I don't know what the light bulb is for . I didn't i install that into my {pause} PowerPoint presentation .
  Grad A: It opens the Assistant that tells you that the font type is too small .
  Grad B: Ah .
  Grad A: Do you wanna try ?
  Grad D: Ach u
  Grad B: I 'd prefer not to .
  Grad A: OK . Continue .
  Grad D: It 's a needless good idea . Is that the idea ?
  Grad A: Why are you doing this in this mode and not in the presentation mode ?
  Grad D: OK .
  Grad B: Because I 'm gonna switch to the JavaBayes program
  Grad A: Oh ! OK . Of course . Mm - hmm .
  Grad B: and then {pause} if I do that it 'll mess everything up .
  Grad D: I was wondering .
  Grad B: Is that OK ?
  Grad D: Yeah , it 's OK .
  Grad A: Sure .
  Grad C: Can you maximize the window ?
  Grad D: Proceed .
  Grad B: You want me to {disfmarker} Wait , what do you want me to do ?
  Grad C: Can you maximize the window so all that stuff on the side isn't {disfmarker} doesn't appear ?
  Grad A: No , It 's OK . It 's {disfmarker} It 'll work .
  Grad B: Well I can do that , but then I have to end the presentation in the middle so I can go back to open up
  Grad C: OK , fine .
  Grad B: Here , let 's see if I can {disfmarker}
  Grad C: Alright .
  Grad D: Very nice .
  Grad B: Is that better ? OK .
  Grad C: Yeah .
  Grad B: Uh {disfmarker} I 'll also get rid of this " Click to add notes " . OK .
  Grad D: Perfect .
  Grad B: So then the features we decided {disfmarker} or we decided we were {disfmarker} talked about , right ? Uh the {disfmarker} the prosody , the discourse , {pause} verb choice . You know . We had a list of things like " to go " and " to visit " and what not . The " landmark - iness " of uh {disfmarker} I knew you 'd like that .
  Grad D: Nice coinage .
  Grad B: Thank you . uh , of a {disfmarker} of a building . Whether the and this i we actually have a separate feature but I decided to put it on the same line {pause} for space . " Nice walls " {vocalsound} which we can look up because I mean if you 're gonna {pause} get real close to a building in the Tango mode , right , there 's gotta be a reason for it . And it 's either because you 're in route to something else or you wanna look at the walls . The context , which in this case we 've limited to {pause} " business person " , " tourist " , or {pause} " unknown " , the time of day , and " open to suggestions " , isn't actually a feature . It 's {pause} " We are open to suggestions . "
  Grad D: Right . can I just ask the nice walls part of it is that {vocalsound} uh , in this particular domain {disfmarker} you said {disfmarker} be {disfmarker} i it could be on two different lines but are you saying that in this particular domain it happens the {disfmarker} that landmark - iness cor is correlated with
  Grad B: Oh {disfmarker}
  Grad C: No . We have a separate
  Grad B: They 're separate things .
  Grad C: feature .
  Grad D: their being nice w
  Grad B: Yeah .
  Grad D: OK .
  Grad B: I either could put " nice walls " on its own line or " open to suggestions " off the slide .
  Grad C: Like you could have a p
  Grad D: And {disfmarker} and {disfmarker} By " nice " you mean {disfmarker}
  Grad C: You {disfmarker} Like you could have a post office with uh {disfmarker} you know , nice murals or something .
  Grad B: Right .
  Grad D: OK .
  Grad B: Or one time I was at this {disfmarker}
  Grad D: So " nice walls " is a stand in for like architecturally it , uh {disfmarker} significant
  Grad B: But see the thing is , if it 's {disfmarker}
  Grad C: Architecturally appealing from the outside .
  Grad D: or something like that . OK .
  Grad B: Yeah but if it 's architecturally significant you might be able to see it from {disfmarker} Like you m might be able to " Vista " it ,
  Grad A: Mm - hmm .
  Grad B: right ? And be able to {disfmarker}
  Grad A: Appreciate it .
  Grad D: Mm - hmm .
  Grad B: Yeah , versus , like , I was at this place in Europe where they had little carvings of , like , dead people on the walls or something .
  Grad D: Mm - hmm .
  Grad B: I don't remember w
  Grad D: Uh - huh .
  Grad B: It was a long time ago .
  Grad D: There 's a lot of those .
  Grad B: But if you looked at it real close , you could see the {disfmarker} the in intricacy of the {disfmarker} of the walls .
  Grad D: OK . So that count as {disfmarker} counts as a nice wall .
  Grad A: Mm - hmm .
  Grad B: Right .
  Grad D: The {disfmarker} OK . Right .
  Grad A: The {disfmarker}
  Grad D: Something you want to inspect at close range {pause} because it 's interesting .
  Grad B: Exactly .
  Grad D: OK .
  Grad A: Hmm .
  Grad B: Robert ?
  Grad A: Well there {disfmarker} there is a term {pause} that 's often used . That 's " saliency " , or the " salience " of an object . And I was just wondering whether that 's the same as what you describe as " landmark - iness " . But it 's really not . I mean an object can be very salient
  Grad D: Hmm .
  Grad A: but not a landmark at all .
  Grad D: Not a landmark at all . There 's landmark for um , touristic reasons and landmark for I don't know navigational reasons or something .
  Grad A: Yep .
  Grad B: Right .
  Grad C: Yeah , we meant , uh , touristic reasons .
  Grad B: Yeah .
  Grad D: OK .
  Grad A: Hmm .
  Grad B: Right .
  Grad D: OK . but you can imagine maybe wanting the oth both kinds of things there for different um , goals .
  Grad A: Hmm .
  Grad C: Yeah .
  Grad B: Right .
  Grad D: Right ?
  Grad B: But {disfmarker} Yeah . Tourist - y landmarks also happen to be {disfmarker} Wouldn't {disfmarker} couldn't they also be {disfmarker} They 're not exclusive groups , are they ? Like {pause} non - tourist - y landmarks and
  Grad A: Or it can be als
  Grad B: direct navigational {disfmarker}
  Grad D: They 're not mutually exclusive ?
  Grad B: Yeah .
  Grad D: Right .
  Grad B: OK .
  Grad D: Right . Definitely .
  Grad B: OK , So our initial idea was not very satisfying , {pause} because {disfmarker} uh our initial idea was basically all the features pointing to the output node . Uh .
  Grad D: So , a big flat structure .
  Grad B: Right .
  Grad D: Right ?
  Grad C: Yep .
  Grad B: And uh , so we {disfmarker} Reasons being , you know , it 'd be a pain to set up all the probabilities for that . If we moved onto the next step and did learning of some sort , uh according Bhaskara we 'd be handicapped . I don't know belief - nets very well .
  Grad C: Well usually , I mean , you know , N {disfmarker} If you have N features , then it 's two to the N {disfmarker} {pause} or exponential in N .
  Grad B: And they wouldn't look pretty . So .
  Grad C: Yeah , they 'd all be like pointing to the one node .
  Grad A: Mm - hmm .
  Grad B: Uh . So then our next idea was to add a middle layer , right ? So the thinking behind that was {vocalsound} we have the features that we 've drawn {pause} from the communication of some {disfmarker} Like , the someone s The person at the screen is trying to communicate some abstract idea , like " I 'm {disfmarker} " the {disfmarker} the abstract idea being " I am a tourist I want to go {pause} to this place . " Right ? So we 're gonna set up features along the lines of where they want to go and {pause} what they 've said previously and whatnot . And then we have {pause} the means {vocalsound} that they should use . Right ? but the middle thing , we were thinking along the lines of maybe trying to figure out , like , the concept of whether they 're a tourist {pause} or {pause} whether they 're running an errand or something like that along those lines . Or {disfmarker} Yes , we could things we couldn't extract the {disfmarker} from the data , the hidden variables . Yes , good . So then the hidden variables {disfmarker} hair variables we came up with were whether someone was on a tour , running an errand , or whether they were in a hurry , because we were thinking uh , if they were in a hurry there 'd be less likely to {disfmarker} like {disfmarker} or th
  Grad C: Want to do Vista ,
  Grad B: Right .
  Grad C: right ? Because {pause} if you want to view things you wouldn't be in a hurry .
  Grad B: Or they might be more likely to be using the place that they want to go to as a {disfmarker} like a {pause} navigational point to go to another place .
  Grad D: Mm - hmm .
  Grad B: Whether the destination was their final destination , whether the destination was closed . Those are all {disfmarker} And then " Let 's look at the belief - net " {comment} OK . So that means that I should switch to the {pause} other program . Um right now it 's still kind of {pause} in a toy {pause} version of it , because we didn't know the probabilities of {disfmarker} {pause} or {disfmarker} Well I 'll talk about it when I get the picture up .
  Grad A: No one knows it .
  Grad B: OK . So this right {disfmarker} what we {disfmarker} Let 's see . What happens if I maximize this ? There we go . But uh {disfmarker} So . The mode {pause} basically has three different {pause} outputs . The probability {disfmarker} whether the probability of a Vista , Tango , or Enter . Um {disfmarker} The " context " , we simplified . Basically it 's just the businessman , the tourist , unknown . " Verb used " is actually personally amusing mainly because it 's {disfmarker} it 's just whether the verb is a Tango verb , an Enter verb , or a {pause} Vista verb .
  Grad C: Yeah , that one needs a lot of {disfmarker}
  Grad D: And are those mutually exclusive sets ?
  Grad B: No .
  Grad C: Not at all . That 's {disfmarker} that {disfmarker} that needs a lot of work .
  Grad D: Right .
  Grad C: But uh {vocalsound} {pause} that would 've made the probably significantly be more complicated to enter ,
  Grad D: Got it . Uh - huh .
  Grad C: so we decided that for the purposes of this {pause} it 'd be simpler to just have three verbs .
  Grad D: Yeah . Simple .
  Grad B: Yeah .
  Grad D: Stab at it . Yep .
  Grad B: Right . Um {disfmarker} Why don't you mention things about this , Bhaskara , that I am {disfmarker} not {disfmarker} that are not coming to my mind right now .
  Grad C: OK , so {disfmarker} Yeah , so note the four nodes down there , the {disfmarker} sort of , the things that are not directly extracted . Actually , the five things . The " closed " is also not directly extracted I guess , from the uh {disfmarker}
  Grad B: Well i it 's {disfmarker}
  Grad C: Hmm .
  Grad D: From the {pause} utterance ?
  Grad B: it 's so it sort of is
  Grad C: Actually , no , wait .
  Grad B: because it 's {disfmarker} because have the {disfmarker} the time of day
  Grad C: It is . OK , " closed " sort of is .
  Grad B: and the close it just had the {disfmarker} er and what time it closed .
  Grad C: Right , so f Right , but the other ones , the final destination , the whether they 're doing business , whether they 're in a hurry , and whether they 're tourists , that kind of thing is all uh {vocalsound} sort of {disfmarker} you know probabilistically depends on the other things .
  Grad D: Inferred from the other ones ?
  Grad C: Yeah .
  Grad D: OK .
  Grad C: And the mode , you know , depends on all those things only .
  Grad B: Yeah the {disfmarker} {pause} the actual parse is somewhere up around in here .
  Grad C: Yeah . So we haven't uh , managed {disfmarker} Like we don't have nodes for " discourse " and " parse " , although like in some sense they are parts of this belief - net .
  Grad D: Mm - hmm .
  Grad C: But uh {disfmarker} {vocalsound} The idea is that we just extract those features from them , so we don't actually have a node for the entire parse ,
  Grad D: Mm - hmm .
  Grad B: Right .
  Grad C: because we 'd never do inference on it anyway , so .
  Grad D: So some of the {disfmarker} the top row of things {disfmarker} What 's {disfmarker} what 's " Disc admission fee " ?
  Grad C: whether they discuss the admission fees . So we looked at the data and in a lot of data people were saying things like {vocalsound} " Can I get to this place ? "
  Grad D: Oh .
  Grad C: " What is the admission fee ? " . So that 's like a huge uh clue that they 're trying to Enter the place rather than uh to Tango or Vista ,
  Grad D: Uh - huh .
  Grad B: Right .
  Grad D: OK .
  Grad C: so .
  Grad D: I see .
  Grad B: There were {disfmarker} there 'd be other things besides just the admission fee , but {pause} you know , {pause} we didn't have {disfmarker}
  Grad D: Mm - hmm .
  Grad C: That was like our example .
  Grad A: Mm - hmm .
  Grad B: That was the {pause} initial one that we found .
  Grad D: OK . So there are certain cues that are very strong {pause} either lexical or topic - based um , concept cues
  Grad B: From the discourse that {disfmarker} Yeah .
  Grad D: for one of those . And then in that second row {pause} or whatever that row of Time of Day through that {disfmarker} So all of those {disfmarker} Some of them come from the utterance and some of them are sort of {vocalsound} either world knowledge or situational {pause} things .
  Grad B: Right .
  Grad D: Right ? So that you have no distinction between those and OK .
  Grad B: One , uh {disfmarker} Uh . {vocalsound} Um , anything else you want to say Bhaskara ?
  Grad C: Um .
  Grad D: " Unmark @ @ Time of Day "
  Grad C: Yeah , I m I mean {disfmarker}
  Grad A: Yeah . They 're {disfmarker} they 're are a couple of more things .
  Grad B: One thing {disfmarker} uh {disfmarker}
  Grad A: I mean Uh . I would actually suggest we go through this one more time so we {disfmarker} we all uh , agree on what {disfmarker} what the meaning of these things is at the moment and maybe {vocalsound} what changes we {disfmarker}
  Grad B: Yeah , th OK . so one thing I {disfmarker} I 'm you know unsure about , is how we have the discus uh {disfmarker} the " admission fee " thing set up . So one {pause} thing that we were thinking was {vocalsound} by doing the layers like this , Uh {disfmarker} we kept um {disfmarker} things from directly affecting the mode {pause} beyond the concept , but you could see perhaps discus the " admission fee " going directly to the mode pointing at " Enter " ,
  Grad A: Mm - hmm .
  Grad B: right ? Versus pointing to just at " tourist " ,
  Grad D: Mm - hmm .
  Grad B: OK ?
  Grad D: Mm - hmm .
  Grad B: But we just decided to keep all the things we extracted {pause} to point at the middle and then {pause} down .
  Grad A: Mm - hmm . Why is the landmark {disfmarker} OK . The landmark is facing to the tourists . That 's because we 're talking about landmarks as touristic landmarks not as possible um
  Grad B: Right .
  Grad C: Yeah .
  Grad B: Navigational landmarks ,
  Grad D: Navigational cue .
  Grad A: navigational landmarks
  Grad B: yeah .
  Grad A: so Mm - hmm . Then {disfmarker}
  Grad B: Yeah , that would be {pause} whatever building they referred to .
  Grad D: Prosody .
  Grad C: Right . So let 's see . The variables .
  Grad A: Mm - hmm .
  Grad C: Disc - " admission fee " is a binary thing , " time of day " is like morning , afternoon , night . Is that the deal ? Yeah .
  Grad B: That 's how we have it currently set up ,
  Grad A: Yep .
  Grad B: but it could be , {pause} you know , based upon hour
  Grad C: Yeah . Yeah .
  Grad A: Whatever granularity .
  Grad B: or {pause} dis we could discrete it {disfmarker} des descret - ize it .
  Grad C: Yeah .
  Grad A: Uh - huh .
  Grad C: Yeah .
  Grad D: Mm - hmm .
  Grad C: Yeah . Normally context will include a huge amount of information , but um , we are just using the particular {vocalsound} part of the context which consists of the switch that they flick to indicate whether they 're a tourist or not , I guess .
  Grad A: Yep .
  Grad D: OK . So that 's given in their input .
  Grad B: Right .
  Grad C: So {disfmarker} Right ,
  Grad D: Right ?
  Grad C: so it 's not really all of context . Similarly prosody is not all of prosody but simply {vocalsound} for our purposes whether or not they appear tense or relaxed .
  Grad A: Mm - hmm . that 's very nice , huh ?
  Grad D: OK .
  Grad A: The {disfmarker} the {disfmarker} So the context is a switch between tourist or non - tourist ?
  Grad C: and
  Grad A: Or also unknown ?
  Grad B: Or un {pause} unknown ,
  Grad A: OK .
  Grad B: yeah .
  Grad C: Yeah . Unknown , right ?
  Grad D: So final dest So it seems like that would really help you for doing business versus tourist ,
  Grad C: Which is th Which one ?
  Grad D: but OK . so the the context being um , e I don't know if that question 's sort of in general , " are you {disfmarker} " I mean the {disfmarker} ar ar are do they allow business people to be doing non - business things at the moment ?
  Grad C: Yeah , it does .
  Grad D: OK . So then you just have some probabilities over {disfmarker}
  Grad C: Everything is probablistic , and {disfmarker} There 's always {disfmarker}
  Grad D: OK . {disfmarker} over which which of those it is .
  Grad C: Yeah . Um , right . So then landmark is {disfmarker} Oh , sorry . " Verb used " is like , right now we only have three values , but in general they would be a probability distribution over all verbs .
  Grad D: Mm - hmm .
  Grad C: Rather , let me rephrase that . It {disfmarker} it can take values {vocalsound} in the set of all verbs , that they could possibly use .
  Grad D: Mm - hmm .
  Grad C: Um " nice walls " is binary , " closed " is binary " final destination " , again {disfmarker} Yeah , all those are binary I guess . And " mode " is one of three things .
  Grad A: So , the {disfmarker} the middle layer is also binary ? No .
  Grad C: Yeah , anything with a question mark after it in that picture is a binary node .
  Grad A: Uh . It {disfmarker} Yeah . But all those things without question marks are also binary . Right ?
  Grad C: Which things ?
  Grad A: Nice walls ?
  Grad B: Wi
  Grad D: Mm - hmm .
  Grad C: Oh . " Nice walls " is uh {disfmarker} something that we extract from our world knowledge .
  Grad A: Mm - hmm .
  Grad C: Yeah , a Oh yeah . Sorry . It is binary .
  Grad B: It is binary but it doesn't have question mark because it 's extracted .
  Grad C: That 's true . Yeah . OK , I see your point .
  Grad A: Yeah . OK .
  Grad B: Yeah .
  Grad A: I {disfmarker} I gotcha .
  Grad D: Uh - huh .
  Grad C: Yeah , similarly " closed " , I guess .
  Grad A: So we can either be in a hurry or not , but we cannot be in a medium hurry at the moment ?
  Grad C: Well , we To do that we would add another uh {disfmarker} value for that .
  Grad A: Mm - hmm . OK .
  Grad C: And that would require s updating the probability distribution for " mode " as well .
  Grad A: Mm - hmm .
  Grad C: Because it would now have to like uh {disfmarker} take that possibility into account .
  Grad D: Mm - hmm . Take a conti
  Grad A: Mm - hmm .
  Grad D: So um , of course this will happen when we think more about the kinds of verbs that are used in each cases
  Grad A: Yeah , yeah .
  Grad C: Yeah .
  Grad D: but you can imagine that it 's verb plus various other things that are also not in the bottom layer that would {disfmarker} that would help you {disfmarker} Like it 's a conjunction of , I don't know , you know , the verb used and some other stuff that {disfmarker} that would {vocalsound} {pause} determine {disfmarker}
  Grad C: Right . Other syntactic information you mean ?
  Grad D: Yeah . Exactly .
  Grad C: Yeah .
  Grad D: Um .
  Grad A: well the {disfmarker} the {disfmarker} sort of the landmark is {disfmarker} is sort of the object right ? the argument in a sense ?
  Grad D: Usually . I {disfmarker} I don't know if that 's always the case I {disfmarker} I guess haven't looked at the data as much as you guys have . So . Um .
  Grad A: that 's always warping on something {disfmarker} some entity ,
  Grad D: Mm - hmm . Mm - hmm .
  Grad A: and um {disfmarker} Uh maybe at this stage we will {disfmarker} we do want to {disfmarker} uh sort of get {disfmarker} uh modifiers in there
  Grad B: Hmm . Yeah .
  Grad A: because they may also tell us whether the person is in a hurry or not
  Grad B: I want to get to the church quickly ,
  Grad C: Yeah .
  Grad D: Mm - hmm .
  Grad B: and uh {disfmarker}
  Grad C: Yeah , right .
  Grad D: That would be a cue .
  Grad A: what 's the fastest way
  Grad C: Yeah , correct .
  Grad D: Mm - hmm . Um . OK .
  Grad B: Right . Excellent . Do we have anything else to say about this ?
  Grad C: We can do a little demo .
  Grad B: Oh the Yeah , we could . But the demo doesn't work very well .
  Grad A: No , then it wouldn't be a demo I was just gonna s
  Grad C: I mean {disfmarker} We can do a demo in the sense that we can um , {vocalsound} just ob observe the fact that this will , in fact do inference .
  Grad B: Observe nodes .
  Grad C: So we can , you know , set some of the uh nodes and then try to find the probability of other nodes .
  Grad D: Yeah . Go ahead .
  Grad B: OK . Dat - dat - dah . What should I observe ?
  Grad C: Just se set a few of them . You don't have to do the whole thing that we did last time . Just like uh , {vocalsound} maybe the fact that they use a certain verb {disfmarker}
  Grad B: OK .
  Grad C: Actually forget the verb .
  Grad B: OK .
  Grad C: just uh {disfmarker} I don't know , say they discussed the admission fee {disfmarker}
  Grad B: OK .
  Grad C: and uh {disfmarker} {vocalsound} the place has nice walls
  Grad B: I love nice walls , OK ? I 'm a big fan .
  Grad C: and it 's night .
  Grad D: it 's starting to grow on me
  Grad B: And the time of day is night ?
  Grad C: Yeah , no wait . That {disfmarker} that doesn't uh {disfmarker} it 's not really consistent . They don't discuss the admission fee . Make that false .
  Grad B: Alright .
  Grad C: And it 's night .
  Grad B: Oh , they {disfmarker} OK . Oh whoops . I forgot to uh {disfmarker}
  Grad C: That didn't work .
  Grad B: Ach !
  Grad D: I 'd like to do that again .
  Grad B: One thing that bugs me about JavaBayes is you have to click that and do this .
  Grad D: Yeah . That seems kind of redundant but .
  Grad C: OK .
  Grad B: That all you want ?
  Grad C: Yes .
  Grad B: OK . So let 's see . I want {pause} to query ,
  Grad C: " Go " and , right , " query " .
  Grad B: right ? the mode . OK , and then on here {disfmarker} So let 's see .
  Grad C: So that {pause} is the probability that they 're Entering , Vista - ing or Tango - ing .
  Grad D: Mm - hmm .
  Grad B: Yeah .
  Grad C: And uh {disfmarker}
  Grad D: So slightly {pause} biased {pause} toward " Tango " ing
  Grad C: Yeah .
  Grad B: If it 's night time , {pause} they have not discussed admission fee , and the n walls are nice .
  Grad D: OK .
  Grad B: So , yeah . I guess that {pause} sort of makes sense . The reason I say the {pause} demo doesn't work very well is yesterday we uh {disfmarker} {pause} observed everything in favor of {pause} taking a tour , and it came up as " Tango " , right ? Over and over again . We couldn't {disfmarker} we couldn't figure out how to turn it off of " Tango " .
  Grad D: So . Uh - huh .
  Grad C: It loves the Tango .
  Grad D: Huh ! Um .
  Grad C: Well , that 's obviously just to do with our probabilities .
  Grad B: Yeah , yeah .
  Grad C: Like , {pause} we totally hand - tuned the probabilities ,
  Grad D: Yeah .
  Grad C: right . We were like {vocalsound} " hmm , well if the person does this and this and this , let 's say forty percent for this ,
  Grad D: OK .
  Grad C: fifty per " Like , you know . So obviously that 's gonna happen .
  Grad B: Yeah .
  Grad D: Right .
  Grad A: Yeah but it {disfmarker} it
  Grad D: Maybe the bias toward " Tango " ing was yours , then ?
  Grad B: Yeah ,
  Grad C: Yeah .
  Grad B: that 's {disfmarker} that 's at {disfmarker}
  Grad C: It 's {disfmarker} So we have to like fit the probabilities .
  Grad B: Spent my youth practicing the tango de la muerte .
  Grad D: So , the real case ?
  Grad A: However you know , it {disfmarker} The purpose was not really , at this stage , to come up with meaningful probabilities but to get thinking about that hidden middle layer . And so th
  Grad D: Mm - hmm .
  Grad A: And {disfmarker}
  Grad B: We would actually {disfmarker} I guess once we look at the data more we 'll get more hidden {pause} nodes ,
  Grad A: Mm - hmm .
  Grad C: Yeah .
  Grad B: but I 'd like to see more . Not because it would {pause} expedite the probabilities , cuz it wouldn't . It would actually slow that down tremendously .
  Grad C: Um . Well , yeah , I guess .
  Grad B: But .
  Grad C: Not that much though . Only a little early .
  Grad B: No , I think we should have uh {disfmarker} exponentially more {pause} middle nodes than features we 've extracted . I 'm ju I 'm just jo
  Grad C: OK .
  Grad D: So . Are " doing business " versus " tourist " {disfmarker} They refer to your current task . Like {disfmarker} like current thing you want to do at this moment .
  Grad C: Um . Yeah , well {disfmarker} {vocalsound} That 's {disfmarker} that 's an interesting point . Whether you 're {disfmarker} It 's whether {disfmarker} It 's not {disfmarker}
  Grad D: And are th
  Grad C: I think it 's more like " Are you are tourist ? are you in Ham - like Heidelberg for a {disfmarker} "
  Grad D: Oh , so , I thought that was directly given by the context {pause} switch .
  Grad C: That 's a different thing . What if the context , which is not set , but still they say things like , " I want to go {pause} uh , see the uh {disfmarker} the {disfmarker} the castle and uh , et cetera . "
  Grad A: Is it {disfmarker}
  Grad B: Well the I kind of {pause} thought of " doing business " as more of running an errand type thing .
  Grad C: Yeah . Business on the other hand is , uh , definitely what you 're doing .
  Grad A: So if you run out of cash as a tourist , and {disfmarker} and {disfmarker} and you need to go to the AT
  Grad B: So {pause} i wi th
  Grad D: OK . Oh , I see , you may have a task . wh you have to go get money and so you are doing business at that stage .
  Grad A: Mmm .
  Grad B: Right .
  Grad C: Yeah .
  Grad A: " How do I get to the bank ? "
  Grad D: I see . Hmm .
  Grad C: And that 'll affect whether you want to enter or you if you {disfmarker} kinda thing .
  Grad D: OK . So the " tourists " node {pause} should be {pause} um , very consistent with the context node . Right ? If you say that 's more their {disfmarker} in general what their background is .
  Grad C: Yeah , I think this context node is a bit of a {disfmarker} I don't know , like in d Uh {disfmarker} Do we {pause} wanna have {disfmarker} Like it 's {disfmarker}
  Grad D: Are you assuming that or not ? Like is that to be {disfmarker} I mean if that 's accurate then that would determine tourist node .
  Grad C: If the context were to set one way or another , that like strongly uh um , says something about whether {disfmarker} whether or not they 're tourists .
  Grad D: Mm - hmm .
  Grad C: So what 's interesting is when it 's not {disfmarker} when it 's set to " unknown " .
  Grad D: Mm - hmm . Mm - hmm .
  Grad A: We - what set the {disfmarker} they set the context to " unknown " ?
  Grad D: OK .
  Grad B: OK .
  Grad C: Right now we haven't observed it , so I guess it 's sort of averaging over all those three possibilities .
  Grad A: Mm - hmm .
  Grad D: Mm - hmm .
  Grad B: Right .
  Grad C: But yes , you can set it to un " unknown " .
  Grad A: And if we now do {disfmarker} leave everything else as is the results should be the same ,
  Grad B: Oops .
  Grad A: right ?
  Grad B: No .
  Grad C: Well no , because we {disfmarker} Th - the way we set the probabilities {vocalsound} might not have {disfmarker} Yeah , it 's {disfmarker} it 's an {disfmarker} it 's an issue , right ? Like {disfmarker}
  Grad A: Pretty much the same ?
  Grad C: Yeah , it is . So the issue is that um in belief - nets , it 's not common to do what we did of like having , you know , a d bunch of values and then " unknown " as an actual value . What 's common is you just like don't observe the variable ,
  Grad D: Yeah .
  Grad A: Yep .
  Grad C: right , and then just marginalizes {disfmarker}
  Grad D: Yeah .
  Grad C: But uh {disfmarker} {vocalsound} We didn't do this because we felt that there 'd {disfmarker} I guess we were thinking in terms of a switch that actually {disfmarker}
  Grad B: We were thi Yeah ,
  Grad A: Mm - hmm .
  Grad B: We were th
  Grad C: But uh {disfmarker} I don't know y what the right thing is to do for that . I 'm not {disfmarker} I don't know if I totally am happy with {vocalsound} the way it is .
  Grad A: Why don't we {disfmarker} Can we , um {disfmarker} How long would it take to {disfmarker} to add another {pause} node on the observatory and , um , play around with it ?
  Grad C: Another node on what ?
  Grad B: Uh , well it depends on how many things it 's linked to .
  Grad A: Let 's just say make it really simple . If we create {pause} something that for example would be um {disfmarker} So th some things can be landmarks in your sense but {pause} they {pause} can never be entered ? So for example s a statue .
  Grad C: Good point .
  Grad A: Yeah ?
  Grad B: Right .
  Grad D: Mm - hmm .
  Grad A: So {pause} maybe we wanna have " landmark " {pause} meaning now " enterable landmark " versus , um something that 's simply just a vista point , for example .
  Grad B: Yeah , that 's true .
  Grad A: Yeah ? uh , a statue or um {disfmarker}
  Grad C: So basically it 's addressing a variable that 's " enterable or not " . So like an " enterable , question mark " .
  Grad B: Also {disfmarker} {pause} you know , didn't we have a {pause} size as one ? The size of the landmark .
  Grad C: What ?
  Grad B: Cuz if it 's {disfmarker}
  Grad C: Um . Not when we were doing this ,
  Grad B: Yeah .
  Grad C: but I guess at some point we did .
  Grad B: For some reason I had that {disfmarker} OK , that was a thought that I had at one point but then went away .
  Grad C: So you want to have a {disfmarker} a node for like whether or not it can be entered ?
  Grad A: Well , for example , if we include that , yeah ?
  Grad C: Yeah .
  Grad A: um , accessibility or something , yeah ? " Is it {disfmarker} Can it be entered ? "
  Grad C: Hmm .
  Grad A: then of course , this is {pause} sort of binary as well .
  Grad C: Yeah .
  Grad A: And then um , there 's also the question whether it may be entered . In the sense that , you know , if it 's {pause} Tom {disfmarker} the house of Tom Cruise , you know , it 's enterable but you may not enter it . You know ? You 're not allowed to .
  Grad C: Yeah .
  Grad A: Unless you are , whatever , his {disfmarker} his divorce lawyer or something .
  Grad C: Yeah .
  Grad A: Yeah ? and um {disfmarker} And these are very observable sort of from the {disfmarker} from the ontology sort of things .
  Grad B: Way Does it actually help to distinguish between those two cases though ? Whether it 's practically speaking enterable , or actually physically enterable {pause} or not ?
  Grad A: y y If {disfmarker} If you 're running an errand you maybe more likely to be able to enter places that are usually not {vocalsound} al w you 're not usually {disfmarker} not allowed to uh m
  Grad D: It seems like it would for uh , uh {pause} determining whether they wanna go into it or not .
  Grad B: Well I can see why {disfmarker}
  Grad D: Cuz they {disfmarker}
  Grad A: Let 's get this uh b clearer . S so it 's matrix between if it 's not enterable , period .
  Grad B: Whether it 's a {disfmarker} Whether it 's a public building , and whether it 's {disfmarker} actually has a door .
  Grad A: Yeah , exactly .
  Grad B: OK .
  Grad A: This is sort of uh
  Grad B: So Tom Cruise 's house is {pause} not a public building
  Grad D: Mm - hmm .
  Grad B: but it has a door . But the thing is {disfmarker}
  Grad C: Mm - hmm .
  Grad D: Right .
  Grad B: OK , sh explain to me why it 's necessary {pause} to distinguish between whether something has a door and is {pause} not public . Or , if something {disfmarker} It seems like it 's equivalent to say that it doesn't have a door a {pause} and it {disfmarker}
  Grad A: Mm - hmm .
  Grad B: Or " not public " and " not a door " are equivalent things ,
  Grad A: Yeah .
  Grad B: it seems like in practice .
  Grad A: Right . Yeah . So we would have {disfmarker} What does it mean , then , that we have to {disfmarker} we have an object type statue . That really is an object type . So there is {disfmarker} there 's gonna be a bunch of statues .
  Grad B: Right .
  Grad A: And then we have , for example , an object type , {pause} hmm , that 's a hotel . How about hotels ?
  Grad B: OK .
  Grad A: So , the most famous building in Heidelberg is actually a hotel . It 's the hotel Zum Ritter , which is the only Renaissance building in Heidelberg that was left after {pause} the big destruction and for the Thirty Years War , blah - blah - blah .
  Grad B: Hmm . Does it have nice walls ?
  Grad A: It has wonderful walls . Um - And lots of detail , c and carvings , engravings and so forth ,
  Grad B: Excellent .
  Grad A: so . But , um , it 's still an unlikely candidate for the Tango mode I {pause} must say . But . Um . So {disfmarker} s So if you are a d Well it 's very tricky . So I guess your question is {disfmarker} so far I have no really arg no real argument why to differentiate between statues as {disfmarker} statues and houses of celebrities , from that point of view . Huh . OK . Let {disfmarker} Let 's do a {disfmarker} Can we add , just so I can see how it 's done , uh , a " has door " {pause} property or {disfmarker} ?
  Grad B: OK .
  Grad C: What would it , uh , connect to ? Like , what would , uh , it affect ?
  Grad A: Um , I think , um , it might affect {disfmarker} Oh actually it 's {disfmarker} it {disfmarker} {pause} it wouldn't affect any of our nodes , right ?
  Grad C: What I was thinking was if you had a {disfmarker} like {disfmarker}
  Grad A: Oh it 's {disfmarker} it affects th The " doing business " is certainly not .
  Grad B: You could affect {disfmarker} {pause} Theoretically you could affect " doing business " with " has door " .
  Grad C: Yeah . OK .
  Grad D: Hmm .
  Grad A: It should , um , inhibit that ,
  Grad C: Right .
  Grad B: Let 's see .
  Grad A: right ?
  Grad C: Yeah , I don't know if JavaBayes is nice about that . It might be that if you add a new thing pointing to a variable , you just like {disfmarker} it just overwrites everything . But {pause} you can check .
  Grad B: Well , {pause} we have it saved . So . {vocalsound} We can rel open it up again .
  Grad C: OK . It 's true .
  Grad B: The {pause} safety net .
  Grad D: I think you could just add it . I mean , I have before OK . Whew !
  Grad C: Well that 's fine , but we have to see the function now . Has it become all point fives or not ?
  Grad D: Oh , right .
  Grad B: Let 's see . So this is " has door " Uh , true , false . That 's acceptable . And I want to edit the function going to that , right ? Oh no .
  Grad C: No . This is fine ,
  Grad B: Right . It was fine .
  Grad C: this business .
  Grad B: added this one .
  Grad C: Yep .
  Grad B: This {disfmarker}
  Grad C: What would be nice if it {disfmarker} is if it just like kept the old function for either value but . Nope . Didn't {pause} do it .
  Grad D: Oh .
  Grad B: Oh wait , it might be {disfmarker} Did we w Yes , that 's not good .
  Grad C: That 's {pause} kind of annoying .
  Grad A: OK , so just dis dismiss everything . Close it and {disfmarker} and load up the old state so {pause} it doesn't screw {disfmarker} screw that up .
  Grad B: Let 's see . Oops .
  Grad C: Hmm .
  Grad A: Maybe you can read in ?
  Grad C: Ha - So have you used JavaBayes a lot ?
  Grad D: Yes . Really {vocalsound} I ha I 've {disfmarker} I haven't used it a lot and I haven't used it in the last you know many months so
  Grad C: OK .
  Grad D: um , uh , we can ask someone .
  Grad C: It might be worth uh {disfmarker} asking around .
  Grad D: Um .
  Grad C: Like , we looked at sort of uh {disfmarker} a page that had like a bunch of {disfmarker}
  Grad D: Yeah . Srini {disfmarker}
  Grad C: OK . Yeah , S I guess he 'd be the person .
  Grad D: Srini 's the one to ask I would say .
  Grad C: Yeah .
  Grad D: Um . He might know .
  Grad C: Cuz {disfmarker} Yeah .
  Grad D: And .
  Grad C: I mean in a way this is a lot of good features in Java it 's cra has a GUI and it 's uh {disfmarker}
  Grad D: Mm - hmm .
  Grad C: I guess those are the main two things . It does learning , it has {disfmarker}
  Grad D: Mm - hmm .
  Grad B: No it doesn't , actually .
  Grad D: Yeah .
  Grad B: I didn't think it did learning .
  Grad C: What ?
  Grad B: Maybe it did a little bit of learning ,
  Grad C: OK .
  Grad B: I don't remember .
  Grad C: Oh right . Maybe you 're right . OK . Right . But uh {disfmarker} it 's free .
  Grad B: Which is w quite positive , yeah .
  Grad C: But uh , yeah . Maybe another thing that uh {disfmarker} But I mean its interface is not the greatest . So .
  Grad B: But actually it had an interface .
  Grad D: Mm - hmm .
  Grad B: A lot of them were like , you know .
  Grad D: Yep .
  Grad A: Command line .
  Grad B: Huh .
  Grad A: What is the c code ? Can w can we see that ? How do you write the code
  Grad B: The c
  Grad A: or do you actually never have to write any code there ?
  Grad C: Yeah . There is actually a text file that you can edit . But it 's {disfmarker} You don't have to do that .
  Grad B: There 's like an XML format for {pause} Bayes - nets .
  Grad C: Is it XML ?
  Grad B: The - there is one . I don't know if this uses it .
  Grad C: Oh , I see . No this doesn't use it .
  Grad B: But it {disfmarker}
  Grad C: I didn't think it did .
  Grad B: Yeah , the {disfmarker} the {disfmarker}
  Grad C: You can look at the text file .
  Grad B: Yeah .
  Grad C: But do you have it here ?
  Grad B: Uh , yes I do actually .
  Grad C: Well , maybe you don't .
  Grad B: Let me see .
  Grad C: Oh yes , of course .
  Grad B: Oh man ,
  Grad C: Like , there 's the {disfmarker}
  Grad B: I didn't n {pause} Is there an ampersand in DOS ?
  Grad C: Nope . Just s l start up a new DOS .
  Grad B: We - That 's alright . I can probably double cli click on it .
  Grad C: Or {disfmarker} Yeah , right .
  Grad A: n uh {disfmarker}
  Grad B: Let 's see .
  Grad C: Yep .
  Grad B: Let 's see , come on .
  Grad C: It 'll ask you what you {disfmarker} what it wants {disfmarker} what you want to open it with and see what BAT , I guess .
  Grad B: One of these days , it should open this , theoretically .
  Grad A: Go {disfmarker} Right mouse . Open with .
  Grad B: Oh there we go .
  Grad C: That 's {disfmarker} Oh !
  Grad B: Maybe it was just {disfmarker}
  Grad A: Oh .
  Grad B: Oh ! W Ah , it was dead . To the world .
  Grad D: God !
  Grad B: OK .
  Grad A: Through the old Notepad . That 's my favorite editor .
  Grad B: I like {disfmarker} I like Word Pad because it has the uh {disfmarker} the returns ,
  Grad A: Wordpad ? I {disfmarker}
  Grad B: the carriage returns on some of them .
  Grad A: Mm - hmm . OK .
  Grad B: You know how they get " auto - fills " I guess ,
  Grad A: Mmm - hmm .
  Grad B: or whatever you call it .
  Grad C: Anyway , there it is .
  Grad A: So this is sort of LISP - y ? No .
  Grad C: Uh , Yeah .
  Grad B: It just basically looks like it just specifies a bunch of
  Grad A: Mm - hmm .
  Grad C: Yeah . That 's how actual probability tables are specified .
  Grad B: Yeah .
  Grad C: As , like , lists of numbers .
  Grad D: Mm - hmm .
  Grad C: So theoretically you could edit that .
  Grad D: Mm - hmm .
  Grad B: It just that {disfmarker} it 's {disfmarker}
  Grad C: But {pause} they 're not very friendly .
  Grad D: Mm - hmm .
  Grad B: Yeah the ordering isn't very clear on {disfmarker}
  Grad C: So you 'd have to like figure out {disfmarker} Like you have to go and {disfmarker}
  Grad D: Right . The layout {pause} of the table .
  Grad C: Yeah .
  Grad D: Yeah .
  Grad B: Actually we could write a program that could generate this .
  Grad C: Well I {disfmarker} Yeah . I think so .
  Grad B: Yeah you could .
  Grad D: You could .
  Grad C: it 's not {disfmarker}
  Grad B: We were doing it {disfmarker}
  Grad C: Yeah we can maybe write an interface th for uh entering probability distributions easily , something like {disfmarker} like a little script . That might {vocalsound} be worth it .
  Grad A: And that might do .
  Grad D: Yeah . I actually seem to recall Srini complaining about something to do with Entering probability so this is probably
  Grad C: The other thing is it is in Java
  Grad D: Yeah , it 's {disfmarker} Yeah .
  Grad C: so .
  Grad B: We could {pause} manipulate the source {pause} itself ?
  Grad D: Yeah .
  Grad B: Or {disfmarker}
  Grad A: Do you have the true source files or just the class ?
  Grad B: I don't know if he actually {disfmarker}
  Grad C: Yeah . Uh , yeah . we do
  Grad B: Does he {disfmarker}
  Grad C: I {disfmarker} I saw directory called " source " ,
  Grad B: Oh .
  Grad D: Mm - hmm .
  Grad B: I didn't e
  Grad C: or {disfmarker} Yeah . Go up one ?
  Grad B: Up one . Ah yes , good .
  Grad C: Yeah .
  Grad B: " Source " . That 's {disfmarker} that 's quite nice .
  Grad C: I don't know if it actually manipulate the source , though . That might be a bit complicated .
  Grad A: Mm - hmm .
  Grad C: I think it might {disfmarker} it might be simpler to just {pause} have a script that , you know {disfmarker} It 's , like , friendly ,
  Grad D: The d the data tables .
  Grad C: it allows you enter things well .
  Grad D: Yeah .
  Grad B: Right .
  Grad A: But if th if there is an XML {pause} file that {disfmarker} or format that it can also read {disfmarker} I mean it just reads this , right ? When it starts .
  Grad C: Mm - hmm .
  Grad B: Yeah I know there is an {disfmarker} I was looking on the we web page and he 's updated it for an XML version of I guess Bayes - nets . There 's a Bayes - net spec for {disfmarker} in XML .
  Grad A: Mm - hmm .
  Grad C: He 's {disfmarker} Like this guy has ?
  Grad B: Yeah .
  Grad C: The JavaBayes guy ? So {disfmarker} but , e he doesn't use it . So in what sense has he updated it ?
  Grad B: Well th you can either {disfmarker} you ca or you can read both .
  Grad C: Oh . I see .
  Grad B: To my understanding .
  Grad C: OK . That would be awesome .
  Grad D: Oh .
  Grad B: Because uh {disfmarker} Well at least the {disfmarker} uh {disfmarker} I could have misread the web page , I have a habit of doing that , but .
  Grad A: OK , wonderful .
  Grad C: OK .
  Grad A: So you got more slides ?
  Grad B: Do I have more slides ? Um yes , one more . " Future Work " . I think every presentation have a should have a " Future Work " slide . But uh it 's basically {disfmarker} we already talked about all this stuff , so .
  Grad C: Um . The additional thing is I guess learning the probabilities , {pause} also . E That 's maybe , I don't know {disfmarker} {vocalsound} If {disfmarker}
  Grad B: Uh that 's future future work .
  Grad C: Does {disfmarker} That 's {disfmarker} Yeah .
  Grad B: Right .
  Grad C: Very future .
  Grad A: Mm - hmm .
  Grad B: And of course if you have a presentation that doesn't {disfmarker} have something that doesn't work at all , then you have " What I learned " , as a slide .
  Grad D: Can't you have both ?
  Grad B: You could . My first approach failed .
  Grad D: Right .
  Grad B: What I learned . OK , so I think that uh our presentation 's finished .
  Grad A: Good .
  Grad B: I know what I like about these meetings is one person will nod , and then the next person will nod , and then it just goes all the way around the room .
  Grad A: So the uh {disfmarker}
  Grad D: I missed my turn .
  Grad B: No I {disfmarker} Earlier I went {nonvocalsound} and Bhaskara went {nonvocalsound} and you did it . You did it .
  Grad A: It 's like yawning .
  Grad D: It 's like yawning .
  Grad A: And this announcement was in stereo .
  Grad C: Ha .
  Grad A: OK . So this means um {disfmarker}
  Grad B: Should I pull up the {pause} net again ?
  Grad D: Yeah . Could you put the {disfmarker} the um , net up again ?
  Grad B: Yes . There we go .
  Grad D: Thanks .
  Grad B: And actually I was {disfmarker} cuz I got a wireless mike on .
  Grad D: So a more general thing than " discussed admission fee " um , could be {disfmarker} I {disfmarker} I 'm just wondering whether the context , the background context of the discourse {vocalsound} might be {disfmarker} I don't know , if there 's a way to define it or maybe you know generalize it some way um , there might be other cues that , say , um , in the last few utterances there has been something that has strongly associated with say one of the particular modes uh , I don't know if that might be {disfmarker}
  Grad A: Mm - hmm . I think we {disfmarker}
  Grad D: uh , and {disfmarker} and into that node would be various {disfmarker} various things that {disfmarker} that could have specifically come up .
  Grad A: I think a {disfmarker} a sort of general strategy here {disfmarker} You know , this is {disfmarker} this is excellent because {disfmarker} um it gets you thinking along these terms {disfmarker} is that maybe we ob we could observe a couple of um discourse phenomena such as the admission fee , and something else and something else , that happened in the discourse before .
  Grad D: Mm - hmm . Right .
  Grad A: And um {disfmarker} let 's make those four . And maybe there are two um {disfmarker} So maybe this could be sort of a separate region of the net , {pause} which has two {disfmarker} {pause} has it 's own middle layer . Maybe this , you know , has some {pause} kind {pause} of um , funky thing that di if this and this may influence these hidden nodes of the discourse which is maybe something that is uh , a more general version of the actual {pause} phenomenon that you can observe . So things that point towards {disfmarker}
  Grad B: So instead of single node , for {pause} like , if they said the word " admission fee " {disfmarker}
  Grad D: Exactly .
  Grad B: " admission fee " , or maybe , you know , " how much to enter "
  Grad D: Yeah .
  Grad B: or you know something , other cues .
  Grad D: Opening hours or something like that .
  Grad B: Exactly . That would all f funnel into one node that would {pause} constitute entrance requirements or something like that .
  Grad A: So " pay a visit " {disfmarker}
  Grad D: Mm - hmm .
  Grad A: uh uh d
  Grad C: Sure .
  Grad A: Yeah ?
  Grad C: Yeah .
  Grad D: I mean it sort of get into plan recognition kinds of things in the discourse . I mean that 's like the bigger um , version of it .
  Grad A: Exactly . Yeah ? And then maybe there are some discourse acts if they happened before , um it 's more for um a cue that the person actually wants to get somewhere else and that you are in a {disfmarker} in a {disfmarker} in a route um , sort of proceeding past these things , so this would be just something that {disfmarker} where you want to pass it . Hmm ? Is that it ? However these are of course then the {disfmarker} the nodes , the observed nodes , for your middle layer . So this {pause} again points to " final destination " , " doing business " , " tourist hurry " and so forth .
  Grad D: Mm - hmm .
  Grad B: OK .
  Grad A: Yeah ? And so then we can say , " OK . we have a whole {pause} region {disfmarker} " {vocalsound} in a e
  Grad D: That 's a whole set of discourse related cues to your middle layer .
  Grad A: Yeah , exactly . And this is just {disfmarker} then just one .
  Grad D: Right ?
  Grad A: So e because at the end the more we um {disfmarker} add , you know , the more spider - web - ish it 's going to become in the middle and the more of hand editing . It 's going to get very ugly . But with this way we could say " OK , these are the {pause} discourse phenomena . They ra may have there own hidden layer {pause} that points to some of {pause} the {disfmarker} the real hidden layer , um or the general hidden layer .
  Grad C: Sure .
  Grad A: And the same we will be able to do for syntactic information , the verbs used , the object types used , modifiers . And maybe there 's a hidden layer for that .
  Grad C: Yep .
  Grad A: And so forth and so forth . Then we have context .
  Grad C: Yeah . So essentially a lot of those nodes can be expanded into little Bayes - nets of their own .
  Grad A: Yep .
  Grad D: Mm - hmm .
  Grad A: Precisely . So .
  Grad B: One thing that 's kind of been bugging me when I {disfmarker} more I look at this is that the {disfmarker} I guess , the fact that the {disfmarker} there 's a complete separation between the {pause} observed features and in the output .
  Grad C: Yeah .
  Grad B: I mean , it makes it cleaner , but then uh {disfmarker} I mean .
  Grad C: That 's true .
  Grad B: For instance if the discourse does {disfmarker}
  Grad D: What do you mean by that ?
  Grad B: well for instance , the " discourse admission fee " {pause} node seems like it should point directly to the {disfmarker}
  Grad D: Uh - huh .
  Grad B: or increase the probability of " enter {pause} directly " versus " going there via tourist " .
  Grad C: Yeah . Or we could like add more , uh , sort of middle nodes . Like we could add a node like do they want to enter it , which is affected by admission fee and by whether it 's closed and by whether it has a door .
  Grad A: Mm - hmm .
  Grad B: Right .
  Grad C: So it 's like {disfmarker} There are {disfmarker} Those are the two options . Either like make an arrow directly or put a new node .
  Grad B: Yeah ,
  Grad D: Hmm .
  Grad B: that makes sense .
  Grad A: Yeah . And if it {disfmarker} if you do it {disfmarker} If you could connect it too hard you may get such phenomenon that {disfmarker} like " So how much has it cost to enter ? " and the answer is two hundred fifty dollars , and then the persons says um " Yeah I want to see it . " Yeah ? meaning " It 's way out of my budget " um {disfmarker}
  Grad B: There are places in Germany where it costs two hundred fifty dollars to enter ?
  Grad A: Um , nothing comes to mind . Without thinking too hard . Um , maybe , yeah of course , um opera premiers .
  Grad B: Really ?
  Grad A: So you know .
  Grad D: Hmm .
  Grad A: Or {disfmarker} or any good old Pink Floyd concert .
  Grad B: I see . If you want to see " The Magic Flute " or something .
  Grad A: Yeah .
  Grad D: Or maybe um , a famous restaurant . or , I don't know . There are various things that you might w not want to eat a {pause} meal there but {pause} your own table .
  Grad B: The Spagos of Heidelberg .
  Grad A: I think that the h I mean nothing beats the {disfmarker} the admission charge prices in Japan . So there , two hundred dollars is {disfmarker} is moderate for getting into a discotheque . You know . Then again , everything else is free then once you 're ins in there .
  Grad C: Really .
  Grad A: Food and drink and so forth . So . I mean . But i you know , i we can {disfmarker} Something {disfmarker} Somebody can have discussed the admission fee and u the answer {pause} is s if we {disfmarker} um , you know , um {disfmarker} still , based on that result is never going to enter that building .
  Grad B: Hmm .
  Grad A: You know ? Because it 's just too expensive .
  Grad B: Oh yeah , I think I see . So the discourse refers to " admission fee " but it just turns out that they change their mind in the middle of the discourse .
  Grad D: Yeah . you have to have some notion of not just {disfmarker} I mean there 's a {disfmarker} there 's change across several turns of discourse
  Grad B: Right .
  Grad A: Mm - hmm .
  Grad D: so {vocalsound} I don't know how {disfmarker} if any of this was discussed {disfmarker} but how i if it all this is going to interact with {vocalsound} whatever general {vocalsound} uh , other {disfmarker} other discourse processing that might be happen .
  Grad A: Mm - hmm .
  Grad C: Yeah . {comment} {nonvocalsound} Yeah .
  Grad D: I mean .
  Grad B: What sort of discourse {pause} processing is uh {disfmarker} are the {disfmarker} How much is built into SmartKom and {disfmarker}
  Grad A: It works like this . The uh , um {disfmarker} I mean . The first thing we get is that {pause} already the intention is sort of t They tried to figure out the intention , right ? simply by parsing it . And this um {disfmarker} m won't differentiate between all modes , yeah ? but at least it 'll tell us " OK here we have something that {disfmarker} somebody that wants to go someplace , now it 's up for us to figure out what kind of going there is {disfmarker} is {disfmarker} {pause} is happening , and um , if the discourse takes a couple of turns before everything {disfmarker} all the information is needed , what happens is you know the parser parses it and then it 's handed on to the discourse history which is , um o one of the most elaborate {disfmarker} elaborate modules . It 's {disfmarker} it 's actually the {disfmarker} the whole memory of the entire system , that knows what {disfmarker} wh who said what , which was {disfmarker} what was presented . It helps an an anaphora resolution and it {disfmarker} and it fills in all the structures that are omitted , so , {pause} um , because you say " OK , {pause} how can I get to the castle ? " Oh , how {disfmarker} how much is it ? " and um " yeah I would like uh {disfmarker} um {disfmarker} to g let 's do it " and so forth . So even without an a ana {pause} anaphora somebody has to make sure that information we had earlier on is still here .
  Grad B: Mm - hmm .
  Grad A: Because not every module keeps a memory of everything that happened . so {vocalsound} whenever the uh , um person is not actually rejecting what happened before , so as in " No I really don't want to see that movie . I 'd rather stay home and watch TV " um {disfmarker} What movie was selected in what cinema in what town is {disfmarker} is going to be sort of added into the disc into the representations every di at each dialogue step , by the discourse model {disfmarker} discourse {pause} model , Yeah , that 's what it 's called . and , um , it does some help in the anaphora resolution and it also helps in coordinating the gesture screen issues . So a person pointing to something on the screen , you know , the discourse model actually stores what was presented at what location on the s on the screen
  Grad B: Hmm .
  Grad A: so it 's a {disfmarker} it 's a rather huge {vocalsound} {disfmarker} huge thing but um {disfmarker} {vocalsound} {comment} um {disfmarker} {pause} {vocalsound} we can {pause} sort of {disfmarker} It has a very clear interface . We can query it whether admission fees were discussed in the last turn and {disfmarker} and the turn before that or you know how {pause} deep we want to search
  Grad B: OK .
  Grad A: um {disfmarker} which is a question . How deep do we want to sear , you know ? Um {disfmarker} but we should {pause} try to keep in mind that , you know , we 're doing this sort of for research , so we {disfmarker} we should find a limit that 's reasonable and not go , you know , all the way back to Adam and Eve . You know , did that person ever discuss admissions fee {disfmarker} fees in his entire life ? And the dialogues are pretty {disfmarker} pretty you know {vocalsound} concise and {disfmarker} Anyway .
  Grad D: So one thing that might be helpful which is implicit in the {pause} use of " admission fee discussion " as a cue for entry , {vocalsound} {pause} is thinking about the plans that various people might have . Like all the different {vocalsound} sort of general schemas that they might be following OK . This person is um , finding out information about this thing in order to go {pause} in as a tourist or finding out how to get to this place {pause} in order to do business . Um , because then {pause} anything that 's a cue for one of the steps {pause} would be slight evidence for that overall plan . Um , I don't know . They 're {disfmarker} in {disfmarker} in non in sort of more traditional AI kinds of plan recognition things you sort of have {vocalsound} {pause} you know , some idea at each turn of agent doing something , " OK , wha what plans is this a {disfmarker} consistent with ? " and then get s some more information and then you see {pause} " here 's a sequence that this sort of roughly fits into " . It {disfmarker} it might be useful here too .
  Grad A: Mm - hmm .
  Grad D: I {disfmarker} I don't know how you know you 'd have to {vocalsound} {pause} figure out what knowl what knowledge representation would work for that .
  Grad A: I mean the {disfmarker} {vocalsound} u u
  Grad B: Hmm .
  Grad A: It 's in the {disfmarker} these {disfmarker} these {disfmarker} these plan schemas . I mean there are some {disfmarker} some of them are extremely elaborate , you know . " What do you need {disfmarker} need to buy a ticket ? "
  Grad D: Mm - hmm .
  Grad A: You know ? and it {disfmarker} it 's fifty steps ,
  Grad D: Mm - hmm . Mm - hmm .
  Grad A: huh ? just for buying a ticket {pause} at a ticket counter , you know , and {disfmarker} and maybe that 's helpful to look at it {disfmarker} to look at those . It 's amazing what human beings can do . W when we talked uh we had the example , you know , of you being uh {disfmarker} a s a person on a ticket counter working at railway station and somebody r runs up to you with a suitcase in his hands , says {vocalsound} New York and you say Track seven , huh ? And it 's because you know that that person actually is following , you know {disfmarker} You execute a whole plan of going through a hundred and fifty steps , you know , without any information other than " New York " , huh ? inferring everything from the context . So , works . Um , even though there is probably no train from here to New York , right ?
  Grad D: Mmm . Not direct .
  Grad B: You 'd uh probably have to transfer in Chicago .
  Grad A: Mm - hmm . But uh {disfmarker} it 's possible . Um , no you probably have to transfer also somewhere else . Right ? Is that t San Francisco , Chicago ?
  Grad B: I think {disfmarker}
  Grad A: Is that possible ?
  Grad B: One time I saw a report on trains , and I think there is a l I don't know if {disfmarker} I thought there was a line that went from somewhere , maybe it was Sacramento to Chicago ,
  Grad A: Mm - hmm .
  Grad B: but there was like a California to Chicago line of some sort .
  Grad A: Hmm .
  Grad B: I could be wrong though . It was a while ago .
  Grad D: The Transcontinental Railroad , doesn't that ring a bell ?
  Grad B: Yeah but I don't know if it 's still {disfmarker}
  Grad D: I think it has to exist somewhere .
  Grad B: They might have blown it up .
  Grad A: Well it never went all the way , right ? I mean you always had to change trains at Omaha ,
  Grad D: Well most of the way .
  Grad A: right ? One track ended there and the other one started at {disfmarker} five meters away from that
  Grad D: Uh . Mm - hmm . Yeah .
  Grad A: and {vocalsound} sort of {disfmarker}
  Grad D: Well . {pause} You seem to know better than we do so .
  Grad A: yeah ? Has anybody ever been on an Amtrak ?
  Grad D: I have . But not transcontinentally .
  Grad B: I 'm frightened by Amtrak myself .
  Grad C: What ? Why ?
  Grad B: I just {disfmarker} They seem to have a lot of accidents on the Amtrak .
  Grad C: Really ?
  Grad A: Their reputation is very bad .
  Grad B: Yeah . Yeah .
  Grad A: huh ? It 's not maybe reality .
  Grad D: It 's not like German trains . Like German trains are really great so .
  Grad A: But {disfmarker} you know , I don't know whether it 's {disfmarker} which ones are safer , you know , statistically .
  Grad D: Um , but they 're faster .
  Grad C: Yeah .
  Grad A: Much faster . Mm - hmm .
  Grad C: And there 's much more of them . Yeah , they 're Yeah , it 's {pause} way better
  Grad A: yeah I used {disfmarker} um Amtrak quite a bit on the east coast and I was surprised . It was actually OK .
  Grad D: Mm - hmm .
  Grad A: You know , on Boston New York ,
  Grad D: Yeah .
  Grad A: New York Rhode Island ,
  Grad C: Yeah .
  Grad A: whatever ,
  Grad C: I 've done that kind of thing .
  Grad A: Boston .
  Grad D: Mm - hmm .
  Grad A: Yeah . But {disfmarker} That 's {pause} a different issue .
  Grad B: This is going to be an interesting transcript .
  Grad A: Hmm ?
  Grad C: I {disfmarker} I want to see what it does with uh " landmark - iness " . That 's {disfmarker}
  Grad B: Yeah .
  Grad D: Let 's all say it a few more times .
  Grad B: It 'd help it figure it out .
  Grad C: So .
  Grad D: Just kidding . Right .
  Grad C: Yeah .
  Grad D: So by the way tha that structure {pause} that Robert drew on the board was like more um , {vocalsound} {pause} cue - type - based , right , here 's like we 're gonna {vocalsound} segment off a bit of stuff that comes from discourse and then {pause} some of the things we 're talking about here are more {disfmarker} you know , we mentioned maybe {pause} if they talk about {pause} um , I don't know , entering or som you know like they might be more task - based .
  Grad B: Hmm .
  Grad D: So I {disfmarker} I don't know if there {disfmarker} There 's obviously some {disfmarker} m more than one way of organizing {pause} the variables into something
  Grad A: I think that um {disfmarker} What you guys did is really nicely sketching out different tasks , and maybe some of their conditions .
  Grad D: so . Mm - hmm .
  Grad A: One task is more likely you 're in a hurry when you do that kind of s doing business ,
  Grad D: Mm - hmm .
  Grad A: and {disfmarker} and less in a hurry when uh {disfmarker} you 're a tourist Um {disfmarker} {vocalsound} tourists may have {disfmarker} never have final destinations , you know because they are eternally traveling around so {vocalsound} maybe what {disfmarker} what {disfmarker} what happened {disfmarker} what might happen is that we do get this sort of task - based middle layer ,
  Grad D: Mm - hmm .
  Grad A: and then we 'll get these sub - middle layers , that are more cue - based .
  Grad D: Mm - hmm . That feed into those ?
  Grad A: Nah ?
  Grad D: Mm - hmm .
  Grad A: Might be {disfmarker} might be a nice dichotomy of {disfmarker} of the world . So , um I suggest w to {disfmarker} for {disfmarker} to proceed with this in {disfmarker} in the sense that {pause} maybe throughout this week the three of us will {disfmarker} will talk some more about maybe segmenting off different regions , and we make up some {disfmarker} {pause} some toy a observable " nodes " {disfmarker} is that what th
  Grad B: Refined y re just refine the {disfmarker}
  Grad A: What 's the technical term ?
  Grad C: OK . For which ?
  Grad A: For the uh {disfmarker} nodes that are observable ? The " outer layer " ?
  Grad C: Just observable nodes ,
  Grad B: The features ,
  Grad C: evidence nodes ?
  Grad B: I don't know , whatever you {disfmarker}
  Grad A: Feature ma make up some features for those {disfmarker} Identify {pause} four regions ,
  Grad C: Yeah .
  Grad A: maybe make up some features for each region and uh {disfmarker} {vocalsound} and uh , uh {disfmarker} and uh {disfmarker} middle layer for those . And then these should then connect somehow to the more plan - based deep space
  Grad C: Yeah .
  Grad B: Basically just refine {pause} some of the more general nodes .
  Grad A: Yep . The - they {disfmarker} they will be aud ad - hoc for {disfmarker} for {disfmarker} for some time to come .
  Grad C: Yeah , this is totally like {disfmarker} The probabilities and all are completely ad - hoc . We need to look at all of them . I mean but , they 're even like {vocalsound} {pause} I mean like , close to the end we were like , uh , you know we were like uh {vocalsound} really ad - hoc .
  Grad D: It 's a even distribution . Like , whatever .
  Grad C: Right ? Cuz if it 's like , uh {disfmarker} If it 's four things coming in , right ? And , say , some of them have like three possibilities and all that . So you 're thinking like {disfmarker} like a hundred and forty four or something {pause} possible things {disfmarker} numbers to enter ,
  Grad D: And {disfmarker} That 's terrible .
  Grad C: right ? So .
  Grad B: Some of them are completely absurd too , like {disfmarker} {vocalsound} they want to enter , but it 's closed ,
  Grad D: That 's uh {disfmarker} {vocalsound} Well {disfmarker}
  Grad B: it 's night time , you know there are tourists and all this weird stuff happens at the line up and you 're like {disfmarker}
  Grad C: Yeah , the only like possible interpretation is that they are like {disfmarker} come here just to rob the museum or {pause} something to that effect .
  Grad B: confused .
  Grad D: In which case you 're supposed to alert the authorities , {vocalsound} and see appropriate action .
  Grad B: Yeah .
  Grad C: Yeah . Yeah , another thing to do , um , is also to , um {disfmarker} I guess to ask around people about other Bayes - net packages . Is Srini gonna be {pause} at the meeting tomorrow , do you know ?
  Grad D: Maybe .
  Grad A: The day after tomorrow .
  Grad C: Wait {disfmarker}
  Grad D: Quite possibly .
  Grad A: Wednesday .
  Grad C: Day after tomorrow .
  Grad D: Oh , oh , sorry .
  Grad C: Yeah .
  Grad D: Sorry , Wednesday ,
  Grad B: Who 's talking on Wednesday ?
  Grad C: Maybe we can ask him about it .
  Grad D: yeah . Mmm .
  Grad B: I haven't {disfmarker} J Jerry never sent out a {disfmarker} sent out an email , did he , ever ?
  Grad C: No . But he mentioned at the last meeting that someone was going to be talking , I forget who .
  Grad A: Oh , isn't Ben ?
  Grad C: Uh .
  Grad D: Ben ?
  Grad A: Ben , then ,
  Grad D: I think it 's Ben actually ,
  Grad A: Ben .
  Grad B: Ah !
  Grad D: yeah , um , giving his job talk I think . um , Sorry . I was just reading the screen .
  Grad A: OK .
  Grad B: Yeah .
  Grad C: Oh .
  Grad A: So the uh {disfmarker} That will be one {disfmarker} one thing we could do . I actually uh , have {disfmarker} Um , also we can uh , start looking at the SmartKom tables and I will {disfmarker}
  Grad B: Right .
  Grad A: I actually wanted to show that {pause} to you guys now but um .
  Grad B: Do you want to {pause} trade ?
  Grad A: Um , no I {disfmarker} I actually made a mistake because it {disfmarker} it fell asleep and when Linux falls asleep on my machine it 's {disfmarker} it doesn't wake up ever , so I had to reboot
  Grad D: Oh , no .
  Grad A: And if I reboot without a network , I will not be able to start SmartKom , because I need to have a network .
  Grad B: Uh {disfmarker}
  Grad A: So we 'll do that t maybe uh {disfmarker}
  Grad C: But . OK . But once you start {disfmarker} sart start SmartKom you can be on {disfmarker} You don't have to be on a network anymore . Is that the deal ?
  Grad A: Yep .
  Grad C: Ah , interesting .
  Grad B: Why does SmartKom need a network ?
  Grad A: Um {disfmarker} {vocalsound} it looks up some stuff that , {pause} you know , is {disfmarker} is that {disfmarker} is in the {disfmarker} written by the operating system only if it {disfmarker} if you get a DHCP request , so it {disfmarker} you know , my computer does not know its IP address , you know ?
  Grad B: Ah .
  Grad A: You know . So . Unless it boots up with networking .
  Grad B: It 's plugged in . Yeah .
  Grad A: And I don't have an IP address , they can't look up {disfmarker} they don't know who localhost is , and so forth and so forth .
  Grad D: Hmm .
  Grad A: Always fun . But it 's a , um , simple solution . We can just um , go downstairs and {disfmarker} and {disfmarker} and look at this , but maybe not today . The other thing um {disfmarker} I will {disfmarker} oh yeah , OK , I have to report {vocalsound} um , data collection . We interviewed Fey ,
  Grad D: Mm - hmm .
  Grad A: She 's willing to do it , meaning be the wizard for the data collection , also {vocalsound} maybe transcribe a little bit , if she has to , but also recruiting subjects , organizing them , and so forth . So that looks good . Jerry however suggested that we should uh have a trial run with her , see whether she can actually do all the uh spontaneous , eloquent and creativeness that we uh expect of the wizard . And I talked to Liz about this and it looks as if Friday afternoon will be the time when we have a first trial run for the data .
  Grad C: So who would be the subject {pause} of this trial run ?
  Grad A: Pardon me ?
  Grad C: Who {disfmarker} Will there be a {disfmarker} Is one {disfmarker} Is you {disfmarker} one of you gonna be the subject ? Like are you {disfmarker}
  Grad A: Um Liz also volunteered to be the first subject , {vocalsound} which I think might be even better than us guys .
  Grad D: Good .
  Grad B: One of us , yeah .
  Grad A: If we do need her for the technical stuff , then of course one of you has to sort of uh {disfmarker} jump in .
  Grad B: I like how we 've {disfmarker} you guys have successfully narrowed it down . " Is one of you going to be the subject ? " Is one of you {disfmarker} jump in .
  Grad D: Reference . I haven't done it yet .
  Grad C: Well I just figured it has to be someone who 's , um , familiar enough with the data to cause problems for the wizard , so we can , uh , see if they 're you know good .
  Grad D: Oh plants ? e u someone who can plant difficult things .
  Grad C: Yeah . I mean that 's what we wanna {pause} check , right ?
  Grad A: Um ,
  Grad D: Well , in this case it 's a p it 's a {pause} sort of testing of the wizard rather than of the subject .
  Grad C: Isn't that what it is ?
  Grad D: It 's uh {disfmarker}
  Grad A: yes w we {disfmarker} we would like to test the wizard , but {vocalsound} you know , if we take a subject that is completely unfamiliar with the task , or any of the set up , we get a more realistic
  Grad C: I guess that would be reasonable .
  Grad D: Yeah .
  Grad A: you know , set up as {disfmarker}
  Grad B: Yeah . I know . That 's probably a good enough test of {disfmarker}
  Grad D: Uh - huh .
  Grad A: Yeah .
  Grad C: Sort of having an actively antagonistic , uh {disfmarker}
  Grad D: Yeah . That might be a little unfair . Um .
  Grad A: Yeah .
  Grad D: I 'm sure if we uh , {disfmarker} You think there 's a chance we might need Liz for , whatever , the technical side of things ? I 'm sure we can get {pause} other people around who don't know anything {pause} um , if we want another subject .
  Grad A: Yeah , yeah .
  Grad D: You know . Like I can drag Ben into it or something . Although he might cause problems but . So , is it a experimental setup for the um , data collection {pause} totally {pause} ready {disfmarker} determined ?
  Grad B: I like that . " Test the wizard . " I want that on a T - shirt .
  Grad A: Um {disfmarker} I think it 's {disfmarker} it 's {disfmarker} it 's {disfmarker} I mean {disfmarker} experimental setup u on the technical issue yes , except we st I think we still need uh {disfmarker} a recording device for the wizard , just a tape recorder that 's running in a room .
  Grad D: Mm - hmm .
  Grad A: But um {disfmarker} in terms of specifying the scenario , {vocalsound} um {disfmarker} uh {disfmarker} uh {disfmarker} we 've gotten a little further
  Grad D: Mm - hmm .
  Grad A: but um {disfmarker} we wanted to wait until we know who is the wizard , and have the wizard partake in the {pause} ultimate {pause} sort of definition probe . So {disfmarker} so if {disfmarker} if on Friday it turns out that she really likes it and {disfmarker} and we really like her , then nothing should stop us from sitting down next week and {vocalsound} {comment} getting all the details completely figured out .
  Grad D: Mm - hmm .
  Grad A: And um {disfmarker}
  Grad D: OK . So the ideal task um , {pause} will have {pause} whatever I don't know how much the structure of the evolving Bayes - net {vocalsound} {pause} will af affect {disfmarker} Like we wanna {disfmarker} we wanna be able to collect {vocalsound} as much of the variables that are needed for that ,
  Grad A: Mmm - yea - some .
  Grad D: right ? in the course of the task ? Well not all of them but you know .
  Grad A: Bu - e e e I 'm even {disfmarker} This {disfmarker} this Tango , Enter , Vista is sort of , itself , an ad - hoc scenario .
  Grad D: Mm - hmm . Mm - hmm .
  Grad A: The {disfmarker} the basic u um idea behind the uh {pause} data collection was the following . The data we get from Munich is {pause} very command line , simple linguistic stuff .
  Grad D: Mm - hmm .
  Grad A: Hardly anything complicated . No metaphors whatsoever .
  Grad D: Mm - hmm .
  Grad A: Not a rich language . So we wanted just to collect data , to get {disfmarker} that {disfmarker} that {disfmarker} that {pause} elicits more , uh , that elicits richer language .
  Grad D: Mm - hmm .
  Grad A: And we actually did not want to constrain it too much ,
  Grad D: Mm - hmm .
  Grad A: you know ? Just see what people say . And then maybe we 'll discover the phenomenon {disfmarker} the phenomena that we want to solve , you know , with {pause} whatever engine we {disfmarker} we come up with . Um . So this {disfmarker} this {disfmarker} this is a parallel track , you know , there {disfmarker} they hopefully meet ,
  Grad D: OK . So in other words this data collection is {pause} more general .
  Grad A: but since {disfmarker}
  Grad D: It could {disfmarker} it could {pause} be used for not just this task .
  Grad A: It should tell us , you know , what kind of phenomenon could occur , it should tell us also maybe something about the difference between people who think they speak to a computer versus people who think they speak to a human being
  Grad D: Mm - hmm .
  Grad A: and the sort of differences there . So it may get us some more information on the human - machine pragmatics , um , that no one knows anything about , as of yesterday . And uh {disfmarker} {vocalsound} nothing has changed {comment} since then , so . Uh . And secondly , now that of course we have sort of started to lick blood with this , and especially since um {disfmarker} {vocalsound} Johno can't stop Tango - ing , {vocalsound} we may actually include , you know , those {disfmarker} those intentions . So now I think we should maybe have at least one navigational task with {disfmarker} with sort of explicit {disfmarker} uh
  Grad D: Mm - hmm .
  Grad A: not ex it 's implicit that the person wants to enter ,
  Grad D: Mm - hmm .
  Grad A: and maybe some task where it 's more or less explicit that the person wants to take a picture ,
  Grad D: Mm - hmm .
  Grad A: or see it or something . So that we can label it . I mean , that 's how we get a corpus that we can label .
  Grad D: Mm - hmm . Exactly .
  Grad A: Whereas , you know , if we 'd just get data we 'd never know what they actually wanted , we 'd get no cues . Yep .
  Grad B: Alrighty .
  Grad C: OK .
  Grad A: That was that .
  Grad B: So is this the official end of the meeting now ?
  Grad C: Yep .
  Grad D: Looks like it .
  Grad C: So what 's " Economics , the fallacy " ?
  Grad A: Ma
  Grad B: I just randomly {pause} label things . So that has nothing to do with economics or anything .
  Grad C: Oh , really ?
  Grad A: Maybe we ought to switch off these things before we continue .
  Grad C: OK .
  Grad D: OK . Switching o
input_prefix: Summarize the meeting