E: Is the intention implicit in the scenario that 's given ?
E: Like , do the D: It is , if they have these tasks that they 're supposed to E: Yeah , I just wasn't sure to what level of detail the task was .
D: to to give D: Yeah ,
D: uh B: Mm - hmm .
D: Right .
B: n No one is , at the moment .
D: Right .
E: OK .
C: So , we that 's part of what we 'll have to figure out .
D: Right .
D: Mm - hmm .
C: But , uh ,
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 , uh , figure out both the intention ,
C: and set the context ,
C: and know what language was used .
C: So let 's suppose that we can get that kind of data .
C: Um .
C: The issue is , can we find a way to , basically , featurize it
C: so that we get some discrete number of features so that , uh , 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 .
D: w What are the t three intentions ?
D: Is it to go there , to see it , and B: To come as close as possible to it .
C: Th - the terminology we 're using is to D: Yeah , it 's @ @ .
C: Go back .
C: To v
D: OK .
C: to View it .
C: OK ?
C: To Enter it .
C: Now those It seems to me those are cl you c you have no trouble with those being distinct .
C: " Take a picture of it "
D: Mm - hmm .
C: you you might well want to be a really rather different place than entering it .
D: Mm - hmm .
C: And , for an object that 's at all big , uh , sort of getting to the nearest part of it uh , could be quite different than either of those .
D: Mm - hmm .
D: Mm - hmm .
D: Mm - hmm .
C: Just sort of D: OK , so now I understand the referent of Tango mode .
E: See , I would have thought it was more of a waltz .
D: I didn't get that before .
B: S
B: To " Waltz " it ?
D: Yeah , like , how close are you gonna be ?
C: Well .
D: Like , Tango 's really close .
E: Yeah , cuz a tango E: Yeah .
C: Well , anyway .
F: All these
C: So F: So , like , the question is how what features can like , do you wanna try to extract from , say , the parse or whatever ?
C: Right .
F: Like , the presence of a word or the presence of a certain uh , stem , or certain construction or whatever .
C: Right .
C: Is there a construction , or the kind of object ,
C: or w uh , anything else that 's in the si It 's either in the in the s the discourse itself or in the context .
C: So if it turns out that , whatever it is , you want to know whether the person 's uh , a tourist or not , OK ? that becomes a feature .
C: Now , how you determine that is another issue .
C: 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 , uh , that would give you a lot of discriminatory power
C: and then just have a little section in your belief - net that said , " pppt ! "
C: Though sin f in the short run , you 'd set them ,
F: Mm - hmm .
C: and see ho how it worked ,
C: and then in the longer run , you would figure out how you could derive them . From previous discourse or w any anything else you knew .
F: Right .
F: So , how should What 's the uh , plan ?
F: Like , how should we go about figuring out these C: OK . So , first of all is ,
C: uh , do e either of you guys , you got a favorite belief - net that you 've , you know , played with ?
C: JavaBayes or something ?
F: Oh .
F: No , not really .
C: OK .
C: Well , anyway . f Get one .
C: OK ? So y so one of th one of the things we wanna do is actually , uh , pick a package ,
C: doesn't matter which one ,
C: uh , presumably one that 's got good interactive abilities ,
C: cuz a lot of what we 're gonna be d
C: You know , we don't need the one that 'll solve massive , uh , belief - nets quickly .
C: d w
C: These are not gonna get big in in the foreseeable future .
C: But we do want one in which it 's easy to interact with and , uh , modify .
C: Because i that 's A lot of what it 's gonna be , is , um , playing with this .
C: And probably one in which it 's easy to have , um , what amounts to transcript files .
C: So that if if we have all these cases C: OK ?
C: So we make up cases that have these features ,
C: OK , and then you 'd like to be able to say , " OK , here 's a bunch of cases " C: There 're even ones tha that you can do learning
C: OK ? So you have all their cases and and their results
C: and you have a algorithms to go through and run around trying to set the the probabilities for you .
C: Um ,
C: probably that 's not worth it .
C: I mean , my guess is we aren't gonna have enough data that 's good enough to make the these data fitting ones worth it ,
C: but I don't know .
C: So I would say you guy the first task for you two guys is to um , pick a package .
C: OK , and you wanna it s You know , the standard things
C: you want it stable , you want it C: yeah , @ @ .
C: And , as soon as we have one , we can start trying to , uh , make a first cut at what 's going on .
B: An - Nuh .
C: But it what I like about it is it 's very concrete .
C: OK ? We we have a we know what the outcomes are gonna be ,
C: and we have some some data that 's loose ,
C: we can use our own intuition ,
C: and see how hard it is ,
C: and , importantly , what intermediate nodes we think we need .
C: 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 . That 'd be really interesting .
E: Mm - hmm .
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 .
B: I mean , w we have the For - FrameNet group here ,
B: and we can see what they have found out about those concepts already ,
B: that are contained in the data ,
B: um , you know , to come up with a nice little set of features
B: and um , maybe even means of s uh , extracting them .
B: And and that altogether could also be uh , become a nice paper that 's going to be published somewhere , if we sit down and write it .
B: And um B: When you said JavaBayes belief - net you were talking about ones that run on coffee ?
B: or that are in the program language Java ?
C: No , th It turns out that there is a , uh The new end of Java libraries .
C: OK , and it turns out one called
C: Which is one that fair people around here use a fair amount .
B: Mmm .
B: OK .
C: I have no idea whether that 's C: 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 .
B: Mm - hmm .
C: So that i that 's I think why a lot of people doing research use that .
C: But it may not be C: I have no idea whether that 's the best choice
C: an and there 're plenty of people around , students in the department who , you know , live and breathe Bayes - nets .
C: So , uh ,
D: There 's the m tool kit that um , Kevin Murphy has developed ,
C: Right .
C: It 's OK .
D: which might be useful too .
F: Right .
C: So , yeah , Kevin would be a good person to start with .
D: And it 's available Matlab code .
C: Nancy knows him well .
C: I don't know I don't know whether you guys have met Kevin yet or not ,
B: Mm - hmm .
C: but , uh F: Yeah , I know him .
B: But i
C: Yeah .
B: But since we all probably are pretty sure that , um , the B: For example , this th th the dialogue history is is um , producing XML documents .
B: M - three - L of course is XML .
B: And the ontology that um , uh the student is is constructing for me back in in EML is in OIL and that 's also in XML .
B: And so that 's where a lot of knowledge about bakeries , about hotels , about castles and stuff is gonna come from .
C: Mm - hmm .
C: Yeah .
B: Um , so , if it has that IO capability and if it 's a Java package , it will definitely be able We can couple .
C: Yeah .
C: So , yeah , we 're sort of <nonvocalsound> committed to XML as the kind of , uh , interchange .
C: But that 's , you know , not a big deal .
B: Who isn't ,
B: nuh ?
C: So , in terms of of interchanging in and out of any module we build , It 'll be XML .
C: And if you 're going off to queries to the ontology , for example , you 'll have to deal with its interface .
C: But that 's that 's fine
C: an and um ,
C: all of these things have been built with much bigger projects than this in mind .
C: So they they have worked very hard .
C: It 's kind of blackboards and multi - wave blackboards
C: and ways of interchanging and registering your
C: a And so forth .
C: So ,
C: that I don't think is even worth us worrying about just yet .
C: I mean 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 , uh , XML , um , little descriptors .
C: I believe .
C: I don't I don't see B: Hmm .
B: Yeah . 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 .
B: That 's , of course , easy also to do with , you know , XML .
C: Uh , you could have an X yeah , you could make and XML format for that . Sure .
B: So r
C: That that C: um ,
C: you know , feature value XML format is probably as good a way as any .
C: So it 's als Yeah , I guess it 's also worth , um , while you 're poking around , poke around for XML packages that um , do things you 'd like .
F: Doesn't does SmartKom system have such packages ?
B: Yeah .
C: Sure .
B: The the lib M - three - L library does that .
C: And the question is , d you c you you 'll have to l We 'll have to l
B: It 's also C: That should be ay We should be able to look at that B: No ,
B: u u y um
B: the B: 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 , um , probability factors based on on on on input C: Yeah .
B: which is in file format ,
B: if we , um , get really w wild on this , we may actually want to use some some corpora that other people made
B: and , for example , if if they are in in MATE , then we get X M L documents with discourse annotations , t
F: Mm - hmm .
B: you know , t from the discourse act down to the phonetic level .
B: Um , Michael has a project where you know , recognizing discourse acts
B: and he does it all in MATE ,
B: and so they 're actually annotating data and data and data .
B: 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 .
F: Right .
C: It 'd be nice , but but I I I do I don't wanna count on it .
C: I mean , you can't you can't run your project based on the speculation that that the data will come ,
B: No , no , uh , just for C: and you don't have to actually design the nets .
B: Nuh .
B: Just a back door that I I think we should devote m
C: Could happen .
C: Yeah . So in terms of of the , um the what the SmartKom gives us for M - three - L packages , it could be that they 're fine , or it could be eeh .
C: You don't You know , you don't really like it .
C: So we 're not we 're not abs we 're not required to use their packages .
C: We are required at the end to give them stuff in their format ,
C: but hey .
F: Right .
C: Um ,
C: it 's , uh C: It doesn't control what you do in you know , internally .
E: What 's the time frame for this ?
B: Two days ?
C: Huh ?
B: Two , three days ?
C: Yeah bu w I 'd like that this y yeah , this week , to ha to n to have y guys , uh , you know , pick the y you know , belief - net package
B: No .
C: and tell us what it is , and give us a pointer so we can play with it or something .
F: Sure .
C: And , then as soon as we have it , I think we should start trying to populate it for this problem .
C: Make a first cut at , you know , what 's going on ,
C: and probably the ea easiest way to do that is some on - line way .
C: I mean , you can f figure out whether you wanna make it a web site
C: or You know , how
B: Uh I I I um , OK , I t
B: Yeah .
B: I was actually more joking . With the two or three days .
C: OK , I wasn't .
B: So this was was a usual jo
B: Um , it will take as long as y y yo you guys need for that .
C: Yeah .
C: Right .
B: But um , 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 ,
B: and what it does , and how it does that .
C: Well , y Well , or both of them speak .
F: Sure .
C: We don't care .
B: Yeah , or you can split it up .
B: So , y
F: Hmm .
B: So that will be sort of the assignment for next week , is to to for slides and whatever net you picked
B: and what it can do and and how far you 've gotten .
B: Pppt !
C: Well , I 'd like to also , though , uh , ha have a first cut at what the belief - net looks like .
C: Even if it 's really crude .
C: OK ?
C: So , you know , here a here are E: So we 're supposed to @ @ about features and whatnot ,
E: and C: Right . Yeah .
F: Mm - hmm .
C: And , as I said , what I 'd like to do is ,
C: I mean , what would be really great is you bring it in If if if we could , uh , in the meeting , say , you know , " Here 's the package ,
C: here 's the current one we have , "
C: uh , you know , " What other ideas do you have ? "
C: and then we can think about this idea of making up the data file .
C: Of ,
C: uh , you know , get a t a p tentative format for it ,
C: let 's say XML , that says , l you know , " These are the various scenarios we 've experienced . "
C: We can just add to that
C: and there 'll be this this file of them
C: and when you think you 've got a better belief - net , You just run it against this , um this data file .
F: So we 'll be like , hand , uh , doing all the probabilities .
F: OK .
C: Oh , yeah , unt until we know more .
E: And what 's the relation to this with Changing the table so that the system works in English ?
B: OK .
B: So this is Whi - while you were doing this , I received two lovely emails .
B: The the full NT and the full Linux version are there .
B: I 've downloaded them both ,
B: and I started to unpack the Linux one B: Uh , the NT one worked fine .
B: and I started unta pack the Linux one ,
B: it told me that I can't really unpack it
B: because it contains a future date .
B: So this is the time difference between Germany .
B: I had to wait until one o ' clock this afternoon before I was able to unpack it .
B: Now , um Then it will be my job to get this whole thing running both on Swede and on this machine .
B: And so that we have it .
B: And then um Hopefully that hoping that my urgent message will now come through to Ralph and Tilman that it will send some more documentation along ,
B: we I control p
B: Maybe that 's what I will do next Monday
B: is show the state and show the system and show that .
C: Yeah .
C: Yeah . So the answer , Johno , is that these are , at the moment , separate .
C: Uh , 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 .
E: I guess my question was more about time frame .
E: So we 're gonna do belief - nets this week ,
E: and then C: Oh , yeah . I don't know .
C: n None of this is i n Neither of these projects has got a real tight time - line ,
C: in the sense that over the next month there 's a there 's a deliverable .
E: OK .
C: OK .
C: S so uh , it 's opportu in that sense it 's opportunistic .
C: 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 C: You know , just pppt !
C: go on and do other things .
E: OK .
B: Yeah , but uh but the uh This point is really I think very , very valid that ultimately we hope that that both will merge into a harmonious and , um , wonderful , um , state
B: 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 ,
B: and now we add this little thing to it " ,
B: you know ?
B: whatever ,
B: Johno 's and Bhaskara 's great belief - net ,
B: and we plug it in ,
B: and then for these certain tasks ,
B: and we know that navigational tasks are gonna be a core domain of the new system ,
B: it all all of a sudden it does much better .
B: Nuh ?
B: Because it can produce better answers ,
B: tell the person , as I s showed you on this map ,
B: n you know , produce either you know , a red line that goes to the Vista point
B: or a red line that goes to the Tango point
B: or red line that goes to the door ,
B: which would be great .
B: 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 .
B: Rather than taking him always to the geometric center of a building ,
F: Mmm .
B: which is what they do now .
B: And we even had to take out a bit .
B: Nancy , you missed that part .
B: We had to take out a bit of the road work .
B: So that it doesn't take you to the wall every time .
D: Oh , really ?
B: So . Um B: So this was actually an actual problem that we encountered ,
B: which nobody have has B: because car navigation systems don't really care .
B: You know , they get you to the beginning of the street ,
B: some now do the house number .
D: Hmm .
D: Mm - hmm .
B: But even that is problematic .
B: If you go d If you wanna drive to the SAP in Waldorf ,
B: I 'm sure the same is true of Microsoft ,
B: it takes you to the the address ,
B: whatever , street number blah - blah - blah ,
B: you are miles away from the entrance .
C: Yep .
B: Because the s postal address is maybe a mailbox somewhere .
D: Mm - hmm .
B: Nuh ?