grad a: And you should be able to see which one which one you 're on by , watching the little bars change . grad a: So , actually , if you guys wanna go ahead and read digits now , as long as you 've signed the consent form , that 's alright . grad e: Are we supposed to read digits at the same time ? grad a: No . We 're talking about doing all at the same time but I think cognitively that would be really difficult . grad a: So , when you 're reading the digit strings , the first thing to do is just say which transcript you 're on . You can see the transcript ? There 's two large number strings on the digits ? So you would just read that one . And the pause is just so the person transcribing it can tell where one line ends and the other begins . And I 'll give I 'll read the digit strings first , so can see how that goes . Well , why don't I go ahead and read digit strings and then we can go on from there . All of you look like you 're doing it reasonably correctly , but you want it about two thumb widths away from your mouth , and then , at the corner . And that 's so that you minimize breath sounds , so that when you 're breathing , you don't breathe into the mike . And the short form  , you should read the consent form , but  , the thing to notice is that we will give you an opportunity to edit a all the transcripts . So , if you say things and you don't want them to be released to the general public , which , these will be available at some point to anyone who wants them , you 'll be given an opportunity by email , to bleep out any portions you don't like .  , should I Do you want me to talk at all about why we 're doing this and what this project is ? professor c: yeah . Oh grad e: Does Nancy know that we 're meeting in here ? grad b: I sent an email . So are the people going to be identified by name ? grad a: Well , what we 're gonna we 'll anonymize it in the transcript . So , then in terms of people worrying about , excising things from the transcript , it 's unlikely . Oh , I see , but the a but the but the grad a: Right , so if I said , " Oh , hi Jerry , how are you ? " , we 're not gonna go through and cancel out the " Jerry "s . grad a: so we will go through and , in the speaker ID tags there 'll be , you know , M - one O seven , M - one O eight . grad a: it w  , I don't know a good way of doing it on the audio , and still have people who are doing discourse research be able to use the data . grad a: And so we don't wanna have to do aliases professor c: Right . grad a: So I think that it 's better just as a pro post - process to edit out every time you bash Microsoft . The idea is that you 'd be able to put a PDA at the table at an impromptu meeting , and record it , and then be able to do querying and retrieval later on , on the meeting . So that 's my particular interest , is a portable device to do m  , information retrieval on meetings . And so what we wanted is a room that 's instrumented with both the table top microphones , and these are very high quality pressure zone mikes , as well as the close talking mikes . What the close talk ng talking mikes gives us is some ground truth , gives us , high quality audio , especially for people who aren't interested in the acoustic parts of this corpus . So , for people who are more interested in language , we didn't want to penalize them by having only the far field mikes available . So that 's why we 're recording in parallel with the close talking and the far field at the same time . And then , all these channels are recorded simultaneously and framed synchronously so that you can also do things like , beam - forming on all the microphones and do research like that . Our intention is to release this data to the public , probably through f through a body like the LDC . So because the general environment is so challenging , we decided to to do at least one set of digit strings to give ourselves something easier . And it 's exactly the same digit strings as in TI - digits , which is a common connected digits corpus . grad a: OK , so when the l last person comes in , just have them wear a wireless . So , the most important form is the consent form , so just be s be sure everyone signs that , if they consent . grad b: I 'm sure it 's pretty usual for meetings that people come late , grad a: Yeah . And  , just give me a call , which , my number 's up there when your meeting is over . grad a: And I 'm going to leave the mike here but it 's n  , but I 'm not gonna be on so don't have them use this one . So you guys who got email about this oh f  , Friday or something about what we 're up to . grad e: What was the nature of the email ? professor c: Oh , this was about  , inferring intentions from features in context , and the words , like " s go to see " , or " visit " , or some grad b: Wel - we I  I I professor c: You didn't get it ? grad e: I don't think I did . We could pursue , if we thought it 's it 's worth it but , I think we we will agree on that , to come up with a with a sort of very , very first crude prototype , and do some implementation work , and do some some research , and some modeling . So the idea is if you want to go somewhere , and focus on that object down Oh , I can actually walk with this . Now , we found in our , data and from experiments , that there 's three things you can do . If you want to actually go up or into the tower , you have to go this way , and then through some buildings and up some stairs and so forth . If you actually want to see the tower , and that 's what actually most people want to do , is just have a good look of it , take a picture for the family , you have to go this way , and go up here . grad b: or so That 's ab er , i the street network of our geographic information system . It would always use the closest point to the object , and then the tourists would be faced , you know , in front of a wall , but it would do them absolutely no good . grad e: What 's it what 's it made out of ? grad b: r red limestone . Okay , I This ,  These intentions , we w w we could , if we want to , call it the the Vista mode , where we just want to eh s get the overview or look at it , the Enter mode , and the , well , Tango mode . So But sometimes the the Tango mode is really relevant in the in the sense that , if you want to ,  If you don't have the intention of entering your building , but you know that something is really close to it , and you just want to approach it , or get to that building . Consider , for example , the Post Office in Chicago , a building so large that it has its own zip code . So , I 've looked , through twenty some  , I didn't look through all the data .  , and there there 's  , a lot more different ways in people  , the ways people phrase how to g get if they want to get to a certain place . Maybe I should go back a couple of steps and go through the professor c: No , OK come in , sit down . grad b: Is I I think grad e: No , that one 's already on , I thought he said .  , people , when they w when they want to go to a building , sometimes they just want to look at it . I I gave an example where the point where you end up if you want to look at it is completely different from where if you want to enter it . So , this is sort of how people may ,  may phrase those requests to a a a mock - up system at least that 's the way they did it . And we get tons of of these " how do I get to " , " I want to go to " , but also , " give me directions to " , and " I would like to see " . And  , what we can sort of do , if we look closer a closer at the the data That was the wrong one . This is of course a crucial factor , " what type of object is it ? " So , some buildings you just don't want to take pictures of . Sometimes I found in the  , looking at the data , in a superficial way , I found some s sort of modifiers that that m may also give us a hint , " I 'm trying to get to " Nuh ? " I need to get to " . Sort of hints to the fact that you 're not really sightseeing and and just f there for pleasure and so forth and so on . That whatever it is you 're doing at the moment may also inter influence the interpretation of of a phrase . What we do know , is that the parser we use in the SmartKom system will never differentiate between any of these . So it 's it 's it 's way too crude to d capture those differences in intentions . So , I thought , " Mmm ! Maybe for a deep understanding task , that 's a nice sort of playground or first little thing . " Where we can start it and n sort of look " OK , we need , we gonna get those M - three - L structures . We may need additional part of speech , or maybe just some information on the verb , and modifiers , auxiliaries . And I will try to to sort of come up with a list of factors that we need to get out of there , and maybe we want to get a g switch for the context . So this is not something which we can actually monitor , now , but just is something we can set . And then you can all imagine sort of a a constrained satisfaction program , depending on on what , comes out . We want to have an a structure resulting if we feed it through a belief - net or or something along those lines . We 'd get an inferred intention , we we produce a structure that differentiates between the Vista , the Enter , and the , Tango mode . So we think it 's a well - formed , starter task for this , deeper understanding in the tourist domain . grad f: So , where exactly is the , deeper understanding being done ? Like  , s is it before the Bayes - net ? Is it ,  professor c: Well , it 's the it 's it 's always all of it . But it 's deep enough that you can distinguish between these th three quite different kinds of , going to see some tourist thing . And , so that 's that 's the quote " deep " that we 're trying to get at . And , Robert 's point is that the current front - end doesn't give you any way to Not only doesn't it do it , but it also doesn't give you enough information to do it . It isn't like , if you just took what the front - end gives you , and used some clever inference algorithm on it , you would be able to figure out which of these is going on . So , and this is Bu - I in general it 's gonna be true of any kind of deep understanding , there 's gonna be contextual things , there 're gonna be linguistic things , there 're gonna be discourse things , and they gotta be combined . And , my idea on how to combine them is with a belief - net , although it may turn out that t some totally different thing is gonna work better .  , the idea would be that you , take your You 're editing your slide ? grad b: Yeah . So the thing is , i  , d naively speaking , you 've you 've got a for this little task , a belief - net , which is going to have as output , the conditional pr probability of one of three things , that the person wants to  , to View it , to Enter it , or to Tango with it . And , then the question is there are two questions is , one , where do you get this i information from , and two , what 's the structure of the belief - net ? So what are the conditional probabilities of this , that , and the other , given these things ? And you probably need intermediate nodes . So it may well be that , for example , that , knowing whether Oh , another thing you want is some information abou I think , about the time of day . And , if things are obviously closed , then , you grad b: People won't want to enter it . And , if it 's not obvious , you may want to actually  , point out to people that it 's closed you know , what they 're g going to is closed and they don't have the option of entering it . grad b: s b professor c: So another thing that can come up , and will come up as soon as you get serious about this is , that another option of course is to have a more of a dialogue . So one thing you could do is build a little system that , said , " whenever you got a question like that I 've got one of three answers . grad b: But maybe that 's a false state of the system , that it 's too close to call . You want the you want the ability to a You want the ability to ask , but what you don't wanna do is onl build a system that always asks every time , and i That 's not getting at the scientific problem , grad b:   professor c: and it 's In general you 're you know , it 's gonna be much more complex than that .  , I think also the the the deep understanding part of it is is going to be in there to the extent that we  , want it in terms of our modeling . We can start , you know , basic from human beings , model that , its motions , going , walking , seeing , we can mem model all of that and then compose whatever inferences o we make out of these really conceptual primitives . S so so the way that might come up , if you wanna Suppose you wanted to do that , you might say , "  , as an intermediate step in your belief - net , is there a Source - Path - Goal schema involved ? " OK ? And if so , is there a focus on the goal ? Or is there a focus on the path ? or something . And that could be , one of the conditiona you know , th the In some piece of the belief - net , that could be the the appropriate thing to enter . grad f: So , where would we extract that information from ? From the M - three - L ? professor c: No . See , the M - three - L is not gonna give th What he was saying is , the M - three - L does not have any of that . grad e: The M - three - L is the old SmartKom output ? professor c: Right . professor c: So we have th w we we we have to have a better w way of referring to grad b: The parser output ? professor c:   grad b: " Analyzed speech " I think it 's what they call it , professor c: Yeah . grad b: o th No , actually , intention lattices is what we 're gonna get . professor c: Is - i but they c they call it intention lattice , but tha grad b: In - in a intention lattice k Hypothesis . So , th they 're gonna give us some cr  or We can assume that y you get this crude information . And they don't give you the kind of object , they don't give you any discourse history , if you want to keep that you have to keep it somewhere else . grad e: So , if someone says , " I wanna touch the side of the Powder - Tower " , that would basically , we need to pop up Tango mode and the and the directions ? professor c: If i if Yeah , if it got as simple as that , yeah . But that doesn't necessarily But we 'd have to infer a Source - Path - Goal to some degree for touching the side , right ? grad b: Well  , th the there is a p a point there if I understand you . " Where is the city hall ? " And this do they don't wanna sh see it on a map , or they don't wanna know it 's five hundred yards away from you , or that it 's to the your north . Where is that damn thing ? grad e: And the parser would output grad b: Well , that 's a a question mark . sh A lot of parsers , just ,  That 's way beyond their scope , is of interpreting that . You know ? But  , still outcome w the outcome will be some form of structure , with the town hall and maybe saying it 's a WH focus on the town hall . grad e: I 'm just trying to figure out what the SmartKom system would output , depending on these things . grad b: it will probably tell you how far away it is , at least that 's That 's even what Deep Map does . Because i we can not differentiate , at the moment , between , you know , the intention of wanting to go there or the intention of just know wanting to know where where it is . grad d: People no might not be able to infer that either , right ? Like the fact Like , I could imagine if someone came up to me and asked , " Where 's the city hall ? " , I might say , g ar " Are you trying to get there ? " Because how I describe  , t its location  , p probably depend on whether I think I should give them , you know , directions now , or say , you know , whatever , " It 's half a mile away " or something like that . grad b: because where people ask you , " Where is New York ? " , you will tell them it 's on the East Coast . grad b: Y y eh you won't tell them how to get there , ft you know , take that bus to the airport and blah - blah - blah . grad b: But if it 's the post office , you will tell them how to get there . professor c: But i Go go back to the the  , th grad b: So I w this is " onto " is is knowledge about buildings , professor c: Yeah , that slide . grad b: their opening times , and then t coupled with time of day , this should You know . grad d: So that context was like , their presumed purpose context , i like business or travel , as well as the utterance context , like , " I 'm now standing at this place at this time " . professor c: Yeah , well I think we ought to d a As we have all along , d We we 've been distu distinguishing between situational context , which is what you have as context , and discourse context , grad b:   And , so what we were talking about doing , a a as a first shot , is not doing any of the linguistics . So , the the the reason the belief - net is in blue , is the notion would be  , this may be a bad dis bad idea , but the idea is to take as a first goal , see if we could actually build a belief - net that would make this three way distinction  , in a plausible way , given these We have all these transcripts and we 're able to , by hand , extract the features to put in the belief - net . Saying , " Aha ! here 're the things which , if you get them out of out of the language and discourse , and put them into the belief - net , it would tell you which of these three  , intentions is most likely . " And if to actually do that , build it ,  you know , run it y y run it on the data where you hand - transcribe the parameters .  th th i i if you can't do this task ,  grad b: We need a different , engine . Well it i I if it if it 's the belief - nets , we we 'll switch to you know , logic or some terrible thing , but I don't think that 's gonna be the case . I think that , if we can get the information , a belief - net is a perfectly good way of doing the inferential combination of it . The real issue is , do what are the factors involved in determining this ? And I don't know . grad d: I missed the beginning , but ,  I guess could you back to the slide , the previous one ? So , is it that it 's ,  These are all factors that  , a These are the ones that you said that we are going to ignore now ? or that we want to take into account ? You were saying n professor c: Take them into account . And and it 's clear from the data , like , sorta the correct answer in each case . professor c: Let 's go back to th Let 's go back to the the the slide of data . grad d: That 's that 's the thing I 'm curious ab grad b:  grad d: Like do we know from the data wh which OK . But , since we are designing a a a an , compared to this , even bigger data collection effort , we will definitely take care to put it in there , grad d:   grad b: in some shape , way , form over the other , grad d:   grad b: to see whether we can , then , get sort of empirically validated data . grad b: from this , we can sometimes , you know an and that 's that but that isn't that what we need for a belief - net anyhow ? is sort of s sometimes when people want to just see it , they phrase it more like this ? But it doesn't exclude anybody from phrasing it totally differently , even if they still grad d:   grad b: But then other factors may come into play that change the outcome of their belief - net . And I 'm sure even i the most , sort of , deliberate data collection experiment will never give you data that say , " Well , if it 's phrased like that , the intention is this . grad b: You know , because then , you grad d: u u  , the only way you could get that is if you were to give th the x subjects a task . Right ? Where you have where your , current goal is to grad b: We Yeah ! That 's what we 're doing . grad d:  grad b: But but we will still get the phrasing all over the place . So , I think you all know this , but we are going to actually use this little room grad d:  professor c: and start recording subjects probably within a month or something . So , this is not any lo any of you guys ' worry , except that we may want to push that effort to get information we need . If it turns out that we need data of a certain sort , then the sort of data collection branch can be , asked to do that . And one of the reasons why we 're recording the meeting for these guys is cuz we want their help when we d we start doing  , recording of subjects . No , you you will not have , and there it is , and ,  But you know , y y the ,  grad d: And I think the other concern that has come up before , too , is if it 's  I don't know if this was collected what situation this data was collected in . Was it is it the one that you showed in your talk ? Like people grad b: No , no . So was this , like , someone actually mobile , like s using a device ? grad b: N no , no not i it was mobile but not not with a w a real wizard system . But , is it I guess I don't know The situation of of collecting th the data of , like Here you could imagine them being walking around the city . And then you have all sorts of other c situational context factors that would influence w how to interpret , like you said , the scope and things like that . grad d: If they 're doing it in a you know , " I 'm sitting here with a map and asking questions " , I I would imagine that the data would be really different . But It was never th th the goal of that data collection to to serve for sat for such a purpose . So that 's why for example the tasks were not differentiated by intentionality , grad d:    I 'm sure we can produce some if we need it , that that will help us along those lines . So , to Finding out what , you know , situational con what the contextual factors of the situation really are , you know is an interesting s interesting thing . grad b: u u Sort of I 'm , at the moment , curious and I 'm I 'm s w want to approach it from the end where we can s sort of start with this toy system that we can play around with , grad d:   grad b: so that we get a clearer notion of what input we need for that , grad d:   And then we can start worrying about where to get this input , what what do we need , you know Ultimately once we are all experts in changing that parser , for example , maybe , there 's just a couple three things we need to do and then we get more whatever , part of speech and more construction - type - like stuff out of it . grad e: How exactly does the data collection work ? Do they have a map , and then you give them a scenario of some sort ? grad b: OK . You 're gonna be in here , and somebody And and you see , either th the three - D model , or  , a QuickTime animation of standing u in a square in Heidelberg . So , just off a textbook , tourist guide , to familiarize , yourself with that sort of odd - sounding German street names , like Fischergasse and so forth . Part two is , you 're told that this huge new , wonderful computer system exists , that can y tell you everything you want to know , and it understands you completely . And so you 're gonna pick up that phone , dial a number , and you get a certain amount of tasks that you have to solve . First you have to know find out how to get to that place , maybe with the intention of buying stamps in there . Maybe So , the next task is to get to a certain place and take a picture for your grandchild . It crashes , And grad d: a At the third ? Right then ? grad b: After the third task . And then , a human operator comes on , and and exp apologizes that the system has crashed , but , you know , urges you to continue , you know ? now with a human operator . And so , you have basically the same tasks again , just with different objects , and you go through it again , and that was it . Oh , and one one little bit w And  , the computer you are you are being told the computer system knows exactly where you are , via GPS . And so you have to do some s tell the person sort of where you are , depending on what you see there .  , this is a a a a a bit that I d I don't think we Did we discuss that bit ?  , I just sort of squeezed that in now . grad d: So , in the display you can Oh , you said that you cou you might have a display that shows , like , the grad b: Yeah . grad d: And so , as you grad b: n grad d: Oh , two - D . grad d: So as you move through it that 's - they just track it on the for themselves grad b: Yeah . So grad b: Yeah ?  that would be an an an enormous technical effort , unless we would We can show it walks to , you know . grad b: And you see the label of the name So we get those names , pronunciation stuff , and so forth , and we can change that . So your tasks don't require you to  ,  yo you 're told So when your task is , I don't know , " Go buy stamps " or something like that ? So , do you have to respond ? or does your  , what are you ste what are you supposed to be telling the system ? Like , w what you 're doing now ? or grad b: Well , we 'll see what people do . grad d: There 's no OK , so it 's just like , " Let 's figure out what they would say under the circumstances " . grad b: in both cases it 's gonna be a human , in the computer , and in the operator case . grad b: And we will re there will be some dialogue , you know ? So , you first have to do this , and that , grad d: Yep . But  , maybe the maybe what you 're suggesting Is what you 're suggesting that it might be too poor , the data , if we sort of limit it to this ping pong one t  , task results in a question and then there 's an answer and that 's the end of the task ? You wanna m have it more more steps , sort of ? grad d: Yeah , I I don't know how much direction is given to the subject about what their interaction  , th they 're unfamiliar w with interacting with the system .  , we we have to have this discussion of th the experiment , and the data collection , and all that sorta stuff grad d:  - huh . Sh - Is sh grad d: She started taking the class last year and then didn't  , you know , didn't continue . So , anyway , she 's looking for some more part time work w while she 's waiting actually for graduate school . So we may have someone , to do this , and she 's got you know , some background in in all this stuff . That 's So , Nancy , we 'll have an At some point we 'll have another discussion on exactly wha t t you know , how that 's gonna go . professor c: And  , Jane , but also , Liz have offered to help us do this , data collection and design and stuff . professor c: So , when we get to that we 'll have some people doing it that know what they 're doing . I guess the reason I was asking about the sort of the de the details of this kind of thing is that , it 's one thing to collect data for , I don't know , speech recognition or various other tasks that have pretty c clear correct answers , but with intention  , obviously , as you point out , there 's a lot of di other factors and I 'm not really sure , how how e the question of how to make it a t appropriate toy version of that  , it 's ju it 's just hard . So , obviously it 's a grad e: Yeah , actually I guess that was my question . Is the intention implicit in the scenario that 's given ? Like , do the grad d: It is , if they have these tasks that they 're supposed to grad e: Yeah , I just wasn't sure to what level of detail the task was . professor c: the The problem that I was tr gonna try to focus on today was , let 's suppose by magic you could collect dialogues in which , one way or the other , you were able to , figure out both the intention , and set the context , and know what language was used . The issue is , can we find a way to , basically , featurize it so that we get some discrete number of features so that , when we know the values to all those features , or as many as possible , we can w come up with the best estimate of which of the , in this case three little intentions , are most likely . grad d: w What are the t three intentions ? Is it to go there , to see it , and grad b: To come as close as possible to it . professor c: Th - the terminology we 're using is to grad d: Yeah , it 's @ @ . " Take a picture of it " you you might well want to be a really rather different place than entering it . professor c: And , for an object that 's at all big , sort of getting to the nearest part of it  , could be quite different than either of those . professor c: Just sort of grad d: OK , so now I understand the referent of Tango mode . grad b: S To " Waltz " it ? grad d: Yeah , like , how close are you gonna be ? professor c: Well . So grad f: All these So , like , the question is how what features can like , do you wanna try to extract from , say , the parse or whatever ? professor c: Right . grad f: Like , the presence of a word or the presence of a certain  , stem , or certain construction or whatever . Is there a construction , or the kind of object , or w  , anything else that 's in the si It 's either in the in the s the discourse itself or in the context . So if it turns out that , whatever it is , you want to know whether the person 's  , a tourist or not , OK ? that becomes a feature . But fo for the current problem , it would just be , " OK , if you can be sure that it 's a tourist , versus a businessman , versus a native , " or something , that would give you a lot of discriminatory power and then just have a little section in your belief - net that said , " pppt ! " Though sin f in the short run , you 'd set them , grad f:   professor c: and see ho how it worked , and then in the longer run , you would figure out how you could derive them . So , how should What 's the  , plan ? Like , how should we go about figuring out these professor c: OK . So , first of all is , do e either of you guys , you got a favorite belief - net that you 've , you know , played with ? JavaBayes or something ? grad f: Oh . OK ? So y so one of th one of the things we wanna do is actually , pick a package , doesn't matter which one , presumably one that 's got good interactive abilities , cuz a lot of what we 're gonna be d You know , we don't need the one that 'll solve massive , belief - nets quickly . Because i that 's A lot of what it 's gonna be , is , playing with this . So that if if we have all these cases OK ? So we make up cases that have these features , OK , and then you 'd like to be able to say , " OK , here 's a bunch of cases " There 're even ones tha that you can do learning OK ? So you have all their cases and and their results and you have a algorithms to go through and run around trying to set the the probabilities for you .  , my guess is we aren't gonna have enough data that 's good enough to make the these data fitting ones worth it , but I don't know . OK , and you wanna it s You know , the standard things you want it stable , you want it yeah , @ @ . And , as soon as we have one , we can start trying to , make a first cut at what 's going on . OK ? We we have a we know what the outcomes are gonna be , and we have some some data that 's loose , we can use our own intuition , and see how hard it is , and , importantly , what intermediate nodes we think we need . So it if it turns out that just , thinking about the problem , you come up with things you really need to You know , this is the kind of thing that is , you know , an intermediate little piece in your belief - net . grad b: And it and it may serve as a platform for a person , maybe me , or whoever , who is interested in doing some linguistic analysis .  , w we have the For - FrameNet group here , and we can see what they have found out about those concepts already , that are contained in the data , you know , to come up with a nice little set of features and  , maybe even means of s  , extracting them . And and that altogether could also be  , become a nice paper that 's going to be published somewhere , if we sit down and write it . And  When you said JavaBayes belief - net you were talking about ones that run on coffee ? or that are in the program language Java ? professor c: No , th It turns out that there is a ,  The new end of Java libraries . I have no idea whether that 's  The obvious advantage of that is that you can then , relatively easily , get all the other Java packages for GUIs or whatever else you might want to do . professor c: So that i that 's I think why a lot of people doing research use that . But it may not be I have no idea whether that 's the best choice an and there 're plenty of people around , students in the department who , you know , live and breathe Bayes - nets . So , grad d: There 's the m tool kit that  , Kevin Murphy has developed , professor c: Right . I don't know I don't know whether you guys have met Kevin yet or not , grad b:   grad b: But i But since we all probably are pretty sure that , the professor c: Yeah . And the ontology that  ,  the student is is constructing for me back in in EML is in OIL and that 's also in XML . And so that 's where a lot of knowledge about bakeries , about hotels , about castles and stuff is gonna come from . grad b: so , if it has that IO capability and if it 's a Java package , it will definitely be able We can couple . grad b: Who isn't , nuh ? professor c: So , in terms of of interchanging in and out of any module we build , It 'll be XML . And if you 're going off to queries to the ontology , for example , you 'll have to deal with its interface . But that 's that 's fine an and  , all of these things have been built with much bigger projects than this in mind . It 's kind of blackboards and multi - wave blackboards and ways of interchanging and registering your a And so forth .  if we can get the core of the thing to work , in a way that we 're comfortable with , then we ca we can get in and out of it with , XML , little descriptors . Yeah , I like , for example , the what you said about the getting input from from just files about where you h where you have the data , have specified the features and so forth . professor c: I don't I don't see grad b: That 's , of course , easy also to do with , you know , XML . grad b: So r professor c: That that  , you know , feature value XML format is probably as good a way as any . So it 's als Yeah , I guess it 's also worth , while you 're poking around , poke around for XML packages that  , do things you 'd like . grad f: Doesn't does SmartKom system have such packages ? grad b: Yeah . It 's also professor c: And the question is , d you c you you 'll have to l We 'll have to l That should be ay We should be able to look at that grad b: No , u u y  the What I What sort of came to my mind i is was the notion of an idea that if if there are l nets that can actually lear try to set their own , probability factors based on on on on input professor c: Yeah . grad b: which is in file format , if we , get really w wild on this , we may actually want to use some some corpora that other people made and , for example , if if they are in in MATE , then we get X M L documents with discourse annotations , t you know , t from the discourse act down to the phonetic level . grad b: Michael has a project where you know , recognizing discourse acts and he does it all in MATE , and so they 're actually annotating data and data and data . So if we w if we think it 's worth it one of these days , not not with this first prototype but maybe with a second , and we have the possibility of of taking input that 's generated elsewhere and learn from that , that 'd be nice . professor c: It 'd be nice , but but I I I do I don't wanna count on it .  , you can't you can't run your project based on the speculation that that the data will come , grad b: No , no , just for professor c: and you don't have to actually design the nets . So in terms of of the ,  the what the SmartKom gives us for M - three - L packages , it could be that they 're fine , or it could be eeh . professor c: it 's ,  It doesn't control what you do in you know , internally . grad b:  grad e: What 's the time frame for this ? grad b: Two days ? Two , three days ? professor c: Huh ? Yeah bu w I 'd like that this y yeah , this week , to ha to n to have y guys , you know , pick the y you know , belief - net package grad b: No . professor c: and tell us what it is , and give us a pointer so we can play with it or something . professor c: And , then as soon as we have it , I think we should start trying to populate it for this problem . Make a first cut at , you know , what 's going on , and probably the ea easiest way to do that is some on - line way .  , you can f figure out whether you wanna make it a web site or You know , how grad b:  I I I  , OK , I t Yeah . grad b: But  , maybe it might be interesting if if the two of you can agree on who 's gonna be the speaker next Monday , to tell us something about the net you picked , and what it does , and how it does that . grad b: So that will be sort of the assignment for next week , is to to for slides and whatever net you picked and what it can do and and how far you 've gotten . Pppt ! professor c: Well , I 'd like to also , though , ha have a first cut at what the belief - net looks like . OK ? So , you know , here a here are grad e: So we 're supposed to @ @ about features and whatnot , professor c: Right . professor c: And , as I said , what I 'd like to do is , what would be really great is you bring it in If if if we could , in the meeting , say , you know , " Here 's the package , here 's the current one we have , "  , you know , " What other ideas do you have ? " and then we can think about this idea of making up the data file . Of , you know , get a t a p tentative format for it , let 's say XML , that says , l you know , " These are the various scenarios we 've experienced . " We can just add to that and there 'll be this this file of them and when you think you 've got a better belief - net , You just run it against this ,  this data file . grad e: And what 's the relation to this with Changing the table so that the system works in English ? grad b: OK . I 've downloaded them both , and I started to unpack the Linux one  , the NT one worked fine . and I started unta pack the Linux one , it told me that I can't really unpack it because it contains a future date . Now ,  Then it will be my job to get this whole thing running both on Swede and on this machine . And then  Hopefully that hoping that my urgent message will now come through to Ralph and Tilman that it will send some more documentation along , we I control p Maybe that 's what I will do next Monday is show the state and show the system and show that .  , what one hopes is that when we understand how the analyzer works , we can both worry about converting it to English and worry about how it could ex extract the parameters we need for the belief - net . So we 're gonna do belief - nets this week , and then professor c: Oh , yeah . n None of this is i n Neither of these projects has got a real tight time - line , in the sense that over the next month there 's a there 's a deliverable . If if you know , if we don't get any information for these guys f for several weeks then we aren't gonna sit around , you know , wasting time , trying to do the problem or guess what they You know , just pppt ! go on and do other things . grad b: Yeah , but  but the  This point is really I think very , very valid that ultimately we hope that that both will merge into a harmonious and , wonderful , state where we can not only do the bare necessities , IE , changing the table so it does exactly in English what it does in German , but also that we can sort of have the system where we can say , " OK , this is what it usually does , and now we add this little thing to it " , you know ? whatever , Johno 's and Bhaskara 's great belief - net , and we plug it in , and then for these certain tasks , and we know that navigational tasks are gonna be a core domain of the new system , it all all of a sudden it does much better . Nuh ? Because it can produce better answers , tell the person , as I s showed you on this map , n you know , produce either you know , a red line that goes to the Vista point or a red line that goes to the Tango point or red line that goes to the door , which would be great . So not only can you show that you know something sensible but ultimately , if you produce a system like this , it takes the person where it wants to go .  So this was actually an actual problem that we encountered , which nobody have has because car navigation systems don't really care . grad b: If you go d If you wanna drive to the SAP in Waldorf , I 'm sure the same is true of Microsoft , it takes you to the the address , whatever , street number blah - blah - blah , you are miles away from the entrance . professor c: Probably not then , cuz y you probably can't drop the mail there anyway . So , you two , who 'll be working on this , li are are you gl will you be doing Well ,  are you supposed to just do it by thinking about the situation ? Can you use the sample data ? professor c: Of course they use the sample data . grad d: Is it like Yeah , ho is there more than Is there a lot s of sample data that is beyond what you what you have there ? grad b: There there 's more than I showed , but  , I think this is sort of  , in part my job to look at that and and to see whether there are features in there that can be extracted , grad d: Yeah . grad b: and to come up with some features that are not you know , empirically based on on a real experiment or on on on reality grad d: Right . grad b: but sort of on your intuition of you know , " Aha ! This is maybe a sign for that , grad d:   We can end the meeting and call Adam , and then we wanna s look at some filthy pictures of Heidelberg . professor c: is that OK ? grad b: And that 's why , when it was hit by  , a cannon ball , it exploded . I first thought it had something to do with the material that it w that 's why I asked