Meeting transcript:
speaker: E
text: I guess .
speaker: A
text: OK , we 're on .
speaker: A
text: So just make sure that th your wireless mike is on , if you 're wearing a wireless .
speaker: E
text: Check one .
speaker: E
text: Check one .
speaker: A
text: And you should be able to see which one <disfmarker> which one you 're on by , uh , watching the little bars change .
speaker: B
text: So , which is my bar ?
speaker: B
text: Mah !
speaker: B
text: Number one .
speaker: A
text: Yep .
speaker: E
text: Sibilance .
speaker: E
text: Sibilance .
speaker: A
text: So , actually , if you guys wanna go ahead and read digits now , as long as you 've signed the consent form , that 's alright .
speaker: E
text: Are we supposed to read digits at the same time ?
speaker: A
text: No .
speaker: E
text: Oh , OK .
speaker: A
text: No .
speaker: A
text: Each individually .
speaker: A
text: We 're talking about doing all at the same time
speaker: A
text: but I think cognitively that would be really difficult . <vocalsound> To try to read them while everyone else is .
speaker: E
text: Everyone would need extreme focus .
speaker: A
text: So , when you 're reading the digit strings , the first thing to do is just say which transcript you 're on .
speaker: C
text: Other way .
speaker: C
text: We m We may wind up with ver We <disfmarker> we may need versions of all this garbage .
speaker: B
text: For our stuff .
speaker: C
text: Yeah .
speaker: B
text: Yeah .
speaker: A
text: Um . So the first thing you 'd wanna do is just say which transcript you 're on .
speaker: C
text: Yeah .
speaker: A
text: So .
speaker: A
text: You can see the transcript ?
speaker: A
text: There 's two large number strings on the digits ?
speaker: A
text: So you would just read that one .
speaker: A
text: And then you read each line with a small pause between the lines .
speaker: A
text: And the pause is just so the person transcribing it can tell where one line ends and the other begins .
speaker: A
text: And I 'll give <disfmarker> I 'll read the digit strings first , so can see how that goes .
speaker: A
text: Um .
speaker: A
text: Again , I 'm not sure how much I should talk about <pause> stuff before everyone 's here .
speaker: C
text: Mmm .
speaker: C
text: Well , we have one more coming .
speaker: A
text: OK .
speaker: A
text: Well , why don't I go ahead and read digit strings
speaker: A
text: and then we can go on from there .
speaker: C
text: OK .
speaker: C
text: Well , we can start doing it .
speaker: A
text: Thanks .
speaker: A
text: So , uh , just also a note on wearing the microphones .
speaker: A
text: All of you look like you 're doing it reasonably correctly ,
speaker: A
text: but you want it about two thumb widths away from your mouth ,
speaker: A
text: and then , at the corner .
speaker: A
text: And that 's so that you minimize breath sounds ,
speaker: A
text: so that when you 're breathing , you don't breathe into the mike .
speaker: A
text: Um .
speaker: A
text: Yeah , that 's good .
speaker: A
text: And uh <disfmarker>
speaker: A
text: So , everyone needs to fill out , only once , the speaker form and the consent form .
speaker: A
text: And the short form <disfmarker>
speaker: A
text: I mean , you should read the consent form ,
speaker: A
text: but uh , the thing to notice is that we will give you an opportunity to edit a all the transcripts .
speaker: A
text: 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 , uh , you 'll be given an opportunity by email , uh , to bleep out any portions you don't like .
speaker: A
text: Um .
speaker: A
text: On the speaker form just fill out as much of the information as you can .
speaker: A
text: If you 're not exactly sure about the region ,
speaker: A
text: we 're not exactly sure either .
speaker: A
text: So , don't worry too much about it .
speaker: A
text: The <disfmarker> It 's just self rating .
speaker: A
text: Um .
speaker: A
text: And I think that 's about it .
speaker: A
text: I mean , should I <disfmarker> Do you want me to talk at all about why we 're doing this and what this project is ?
speaker: C
text: Um , yeah .
speaker: A
text: or <disfmarker> ?
speaker: C
text: No . There was <disfmarker> there was <disfmarker> Let 's see .
speaker: E
text: Does Nancy know that we 're meeting in here ?
speaker: C
text: Oh <disfmarker>
speaker: C
text: She got an emai she was notified .
speaker: B
text: I sent an email .
speaker: E
text: Oh yeah , she got an e
speaker: E
text: Yeah , yeah .
speaker: C
text: Whether she knows <vocalsound> is another question .
speaker: C
text: Um .
speaker: C
text: So are the people going to be identified by name ?
speaker: A
text: Well , what we 're gonna <disfmarker> we 'll anonymize it in the transcript .
speaker: C
text: Right .
speaker: A
text: Um , but not in the audio .
speaker: C
text: OK .
speaker: C
text: So , then in terms of people worrying about , uh , excising things from the transcript , it 's unlikely .
speaker: A
text: So the
speaker: C
text: Since it <disfmarker> it does isn't attributed .
speaker: C
text: Oh , I see ,
speaker: C
text: but the a but the <disfmarker> but the <disfmarker>
speaker: A
text: Right , so if I said , " Oh , hi Jerry , how are you ? " , we 're not gonna go through and cancel out the " Jerry "s .
speaker: C
text: Yeah .
speaker: C
text: Sure .
speaker: A
text: Um , so we will go through and , in the speaker ID tags there 'll be , you know , M - one O seven , M - one O eight .
speaker: C
text: Right .
speaker: C
text: Right .
speaker: A
text: Um , but uh ,
speaker: A
text: um , it w uh ,
speaker: A
text: 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 .
speaker: C
text: OK .
speaker: C
text: Mm - hmm .
speaker: C
text: No , I <disfmarker> I wasn't complaining ,
speaker: C
text: I just wanted to understand .
speaker: A
text: Yep .
speaker: A
text: Right .
speaker: C
text: OK .
speaker: B
text: Well , we can make up aliases for each of us .
speaker: A
text: Yeah , I mean , whatever you wanna do is fine ,
speaker: C
text: Right .
speaker: F
text: OK .
speaker: A
text: but we find that <disfmarker>
speaker: A
text: We want the meeting to be as natural as possible .
speaker: C
text: OK .
speaker: A
text: So , we 're trying to do real meetings .
speaker: A
text: And so we don't wanna have to do aliases
speaker: C
text: Right .
speaker: A
text: and we don't want people to be editing what they say .
speaker: B
text: Right .
speaker: C
text: Right .
speaker: A
text: So I think that it 's better just as a pro post - process to edit out every time you bash Microsoft .
speaker: B
text: Mm - hmm .
speaker: A
text: You know ?
speaker: C
text: Right .
speaker: C
text: Um , OK . So why don't you tell us briefly
speaker: C
text: your <disfmarker> give <disfmarker> give your e normal schpiel .
speaker: A
text: OK .
speaker: A
text: So th
speaker: A
text: Um . So this is <disfmarker> The project is called Meeting Recorder
speaker: A
text: and there are lots of different aspects of the project .
speaker: A
text: Um . So my particular interest is in the PDA of the future .
speaker: A
text: This is a mock - up of one .
speaker: A
text: Yes , we do believe the PDA of the future will be made of wood .
speaker: A
text: Um . <comment> The idea is that you 'd be able to put a PDA at the table at an impromptu meeting ,
speaker: A
text: and record it ,
speaker: A
text: and then be able to do querying and retrieval later on , on the meeting .
speaker: A
text: So that 's my particular interest ,
speaker: A
text: is a portable device to do m uh , information retrieval on meetings .
speaker: A
text: Other people are interested in other aspects of meetings .
speaker: A
text: Um . So the first step on that , in any of these , is to collect some data .
speaker: A
text: And so what we wanted is a room that 's instrumented with both the table top microphones ,
speaker: A
text: and these are very high quality pressure zone mikes ,
speaker: A
text: as well as the close talking mikes .
speaker: A
text: What the close talk ng talking mikes gives us is some ground truth ,
speaker: A
text: gives us , um , high quality audio ,
speaker: A
text: um , especially for people who aren't interested in the acoustic parts of this corpus .
speaker: A
text: So , for people who are more interested in language , we didn't want to penalize them by having only the far field mikes available .
speaker: A
text: And then also , um , it 's a very , very hard task in terms of speech recognition .
speaker: A
text: Um . And so , uh , on the far field mikes we can expect very low recognition results .
speaker: A
text: So we wanted the near field mikes to at least isolate the difference between the two .
speaker: A
text: So that 's why we 're recording in parallel with the close talking and the far field at the same time .
speaker: A
text: And then , all these channels are recorded simultaneously and framed synchronously
speaker: A
text: so that you can also do things like , um , beam - forming on all the microphones
speaker: A
text: and do research like that .
speaker: A
text: Our intention is to release this data to the public , um , probably through f through a body like the LDC .
speaker: A
text: And , uh , just make it as a generally available corpus .
speaker: A
text: Um . <vocalsound> There 's other work going on in meeting recording .
speaker: A
text: So , we 're <disfmarker> we 're working with SRI ,
speaker: A
text: with UW ,
speaker: A
text: Um . NIST has started an effort which will include video .
speaker: A
text: We 're not including video , obviously .
speaker: A
text: And uh <disfmarker> and then also , um , a small amount of assistance from IBM .
speaker: A
text: Is also involved .
speaker: A
text: Um . Oh , and the digit strings , this is just a more constrained task .
speaker: A
text: Um . So because the general environment is so challenging , we decided to <disfmarker> to do at least one set of digit strings
speaker: A
text: to give ourselves something easier .
speaker: A
text: And it 's exactly the same digit strings as in TI - digits ,
speaker: A
text: which is a common connected digits corpus .
speaker: A
text: So we 'll have some , um , comparison to be able to be made .
speaker: C
text: OK .
speaker: A
text: Anything else ?
speaker: C
text: No .
speaker: A
text: OK , so when the l last person comes in , just have them wear a wireless .
speaker: A
text: It should be on already .
speaker: A
text: Um .
speaker: A
text: Either one of those .
speaker: A
text: And uh , read the digit strings and <disfmarker> and fill out the forms .
speaker: A
text: So , the most important form is the consent form ,
speaker: A
text: so just be s be sure everyone signs that , if they consent .
speaker: B
text: I 'm sure it 's pretty usual for meetings that people come late ,
speaker: B
text: so you will have to leave what you set .
speaker: A
text: Yeah .
speaker: A
text: Right .
speaker: A
text: And uh , just give me a call ,
speaker: A
text: which , my number 's up there when your meeting is over .
speaker: C
text: Yep .
speaker: A
text: And <disfmarker> I 'm going to leave the mike here
speaker: A
text: but it 's n <nonvocalsound> Uh , but I 'm not gonna be on
speaker: A
text: so don't have them use this one .
speaker: A
text: It 'll just be sitting here .
speaker: B
text: Input ?
speaker: B
text: Yeah .
speaker: B
text: There we go .
speaker: C
text: By the way , Adam , we will be using the , uh , screen as well .
speaker: B
text: Yep .
speaker: C
text: So , you know .
speaker: C
text: Wow !
speaker: C
text: Organization .
speaker: C
text: So you guys who got email about this <pause> oh f uh , Friday or something about what we 're up to .
speaker: E
text: No .
speaker: F
text: No .
speaker: B
text: I got it .
speaker: E
text: What was the nature of the email ?
speaker: C
text: Oh , this was about <pause> um , inferring intentions from features in context , and the words ,
speaker: C
text: like " s go to see " , or " visit " , or some
speaker: B
text: Wel - we I <disfmarker> uh <disfmarker> I <disfmarker> I <disfmarker>
speaker: C
text: You didn't get it ?
speaker: E
text: I don't think I did .
speaker: C
text: I guess these g have got better filters .
speaker: C
text: Cuz I sent it to everybody .
speaker: C
text: You just blew it off .
speaker: E
text: Ah .
speaker: C
text: OK .
speaker: B
text: It 's really simple though .
speaker: B
text: So this is the idea .
speaker: B
text: Um .
speaker: B
text: We could pursue , um , if we thought it 's <disfmarker> it 's worth it but , uh , I think we <disfmarker> we will agree on that , um , to come up with a <disfmarker> with a sort of very , very first crude prototype ,
speaker: B
text: and do some implementation work , and do some <disfmarker> some research , and some modeling .
speaker: B
text: So the idea is if you want to go somewhere ,
speaker: B
text: um , and
speaker: B
text: focus on that object down <disfmarker>
speaker: B
text: Oh , I can actually walk with this .
speaker: B
text: This is nice .
speaker: B
text: down here .
speaker: B
text: That 's the Powder - Tower .
speaker: B
text: Now , um , <vocalsound> we found in our , uh , data and from experiments , that there 's three things you can do .
speaker: B
text: Um , you can walk this way , and come really , really close to it .
speaker: B
text: And touch it .
speaker: B
text: But you cannot enter or do anything else .
speaker: B
text: Unless you 're interested in rock climbing , it won't do you no good standing there .
speaker: B
text: It 's just a dark alley .
speaker: B
text: But you can touch it .
speaker: B
text: If you want to actually go up or into the tower , you have to go this way ,
speaker: B
text: and then through some buildings and up some stairs and so forth .
speaker: B
text: 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 , <comment> you have to go this way , and go up here .
speaker: B
text: And there you have a vre really view <disfmarker> It exploded , the <disfmarker> during the Thirty
speaker: B
text: years - war . Really uh , interesting sight .
speaker: E
text: Mmm .
speaker: B
text: And um , these uh <disfmarker> these lines are , um , paths ,
speaker: B
text: or so That 's ab er , i
speaker: B
text: the street network of our geographic information system .
speaker: B
text: And you can tell that we deliberately cut out this part .
speaker: B
text: Because otherwise we couldn't get our GIS system to take <disfmarker> to lead people this way .
speaker: B
text: It would always use the closest point to the object ,
speaker: B
text: and then the tourists would be faced , you know , in front of a wall ,
speaker: B
text: but it would do them absolutely no good .
speaker: B
text: So , <vocalsound> what we found interesting is , first of all , intentions differ .
speaker: B
text: Maybe you want to enter a building .
speaker: B
text: Maybe you want to see it ,
speaker: B
text: take a picture of it .
speaker: B
text: Or maybe you actually want to come as close as possible to the building .
speaker: B
text: For whatever reason that may be .
speaker: E
text: What 's it <disfmarker> what 's it made out of ?
speaker: B
text: Um , r red limestone .
speaker: E
text: So maybe you would wanna touch it .
speaker: B
text: Yeah , maybe you would want to touch it .
speaker: B
text: Um .
speaker: B
text: Okay , I <disfmarker> This , um <disfmarker> These intentions , we <disfmarker> w w we could , if we want to , call it the <disfmarker> the Vista mode ,
speaker: B
text: where we just want to <disfmarker> eh <disfmarker> s get the overview or look at it ,
speaker: B
text: the Enter mode ,
speaker: B
text: and the , well , Tango mode .
speaker: B
text: I always come up with <disfmarker> with silly names .
speaker: B
text: So this " Tango " means , literally translated , " to touch " .
speaker: B
text: So <disfmarker> But sometimes the <disfmarker> the Tango mode is really relevant in the <disfmarker> in the sense that , um , if you want to , uh <disfmarker>
speaker: B
text: 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 .
speaker: B
text: Consider , for example , the Post Office in Chicago , a building so large that it has its own zip code .
speaker: B
text: So the entrance could be miles away from the closest point .
speaker: B
text: So sometimes it m m m makes sense maybe to d to distinguish there .
speaker: B
text: So , um , I 've looked , uh , through twenty some <disfmarker>
speaker: B
text: Uh , I didn't look through all the data .
speaker: B
text: um , and there <disfmarker> there 's uh , a lot more different ways in people <disfmarker> uh , the ways people phrase how to g get <disfmarker> if they want to get to a certain place .
speaker: B
text: And sometimes here it 's b it 's a little bit more obvious <disfmarker>
speaker: B
text: Um . <vocalsound> Maybe I should go back a couple of steps
speaker: B
text: and go through the <disfmarker>
speaker: C
text: No , OK come in , sit down .
speaker: C
text: If you grab yourself a microphone .
speaker: B
text: You need to sign some stuff and read some digits .
speaker: C
text: Well , you can sign afterwards .
speaker: B
text: O or later .
speaker: E
text: You have to al also have to read some digits .
speaker: C
text: Afterwards .
speaker: D
text: OK . <comment> OK . Afterwards is fine .
speaker: B
text: They are uncomfortable .
speaker: B
text: Mm - hmm .
speaker: D
text: Really small ?
speaker: D
text: OK . I see .
speaker: D
text: OK .
speaker: B
text: Yep .
speaker: D
text: Thank you .
speaker: B
text: OK , but that was our idea .
speaker: C
text: And it <disfmarker> it <disfmarker> it <disfmarker> it it also has to be switched on , Nance .
speaker: B
text: Is <disfmarker>
speaker: E
text: No , that one 's already on , I thought he said .
speaker: B
text: I <disfmarker> I think <disfmarker>
speaker: C
text: It 's on ?
speaker: C
text: OK , good .
speaker: E
text: Yeah .
speaker: D
text: OK . It 's on .
speaker: B
text: OK . That was the idea .
speaker: B
text: Um , people , when they w when they want to go to a building , sometimes they just want to look at it .
speaker: B
text: Sometimes they want to enter it .
speaker: B
text: And sometimes they want to get really close to it .
speaker: B
text: That 's something we found .
speaker: B
text: It 's just a truism .
speaker: B
text: And the places where you will lead them for these intentions are sometimes ex in incredibly different .
speaker: B
text: I <disfmarker> I gave an example where the point where you end up if you want to look at it is completely different from where <disfmarker> if you want to enter it .
speaker: B
text: So , this is sort of how people may , uh <disfmarker> may phrase those requests to a <disfmarker> a <disfmarker> a mock - up system at least that 's the way they did it .
speaker: B
text: And we get tons of <disfmarker> of these " how do I get to " , " I want to go to " ,
speaker: B
text: but also , " give me directions to " , and " I would like to see " .
speaker: B
text: And um , what we can sort of do , if we look closer a closer at the <disfmarker> the data <disfmarker>
speaker: B
text: That was the wrong one .
speaker: B
text: um , we can look at some factors that may make a difference .
speaker: B
text: First of all ,
speaker: B
text: very important ,
speaker: B
text: and um , that <disfmarker> I 've completely forgot that when we talked .
speaker: B
text: This is of course a crucial factor , " what type of object is it ? "
speaker: B
text: So , some buildings you just don't want to take pictures of .
speaker: B
text: Or very rarely .
speaker: B
text: But you usually want to enter them .
speaker: B
text: Some objects are more picturesque ,
speaker: B
text: and you <disfmarker> more f more highly photographed .
speaker: B
text: Then of course the <disfmarker> the actual phrases may give us some idea of what the person wants .
speaker: B
text: Um . Sometimes I found in the <disfmarker> Uh , looking at the data , in a superficial way , I found some s sort of modifiers that <disfmarker> that m may also give us a hint ,
speaker: B
text: um , " I 'm trying to get to "
speaker: B
text: Nuh ?
speaker: B
text: " I need to get to " .
speaker: B
text: Sort of hints to the fact that you 're not really sightseeing and <disfmarker> and just f there for pleasure and so forth and so on .
speaker: B
text: And this leads us straight to the context which also should be considered .
speaker: B
text: That whatever it is you 're doing at the moment may also inter influence the interpretation of <disfmarker> of a phrase .
speaker: B
text: So , this is , uh , really
speaker: B
text: uh , uh , uh <disfmarker>
speaker: B
text: My suggestion is really simple .
speaker: B
text: We start with , um <disfmarker>
speaker: B
text: Now , Let me , uh , say one more thing .
speaker: B
text: What we do know , is that the parser we use in the SmartKom system will never differentiate between any of these .
speaker: B
text: So , basically all of these things will result in the same XML M - three - L structure .
speaker: B
text: Sort of action " go " ,
speaker: B
text: and then an object .
speaker: D
text: Mm - hmm .
speaker: B
text: Yeah ?
speaker: B
text: and a source .
speaker: B
text: So it 's <disfmarker> it 's <disfmarker> it 's way too crude to d capture those differences in intentions .
speaker: B
text: So , I thought , " Mmm !
speaker: B
text: Maybe for a deep understanding task , that 's a nice sort of playground or first little thing . "
speaker: B
text: Where we can start it and n sort of look <disfmarker>
speaker: B
text: " OK , we need , we gonna get those M - three - L structures .
speaker: B
text: The crude , undifferentiated parse .
speaker: B
text: Interpreted input .
speaker: B
text: We may need additional part of speech , or maybe just some information on the verb , and modifiers , auxiliaries .
speaker: B
text: We 'll see .
speaker: B
text: And I will try to <disfmarker> to sort of come up with a list of factors that we need to get out of there ,
speaker: B
text: and maybe we want to get a g switch for the context .
speaker: B
text: So this is not something which we can actually monitor , <vocalsound> now ,
speaker: B
text: but just is something we can set .
speaker: B
text: And then you can all imagine sort of a <disfmarker> a constrained satisfaction program , depending on <disfmarker> on what , um , comes out .
speaker: B
text: We want to have an <disfmarker> a structure resulting
speaker: B
text: if we feed it through a belief - net or <disfmarker> or something along those lines . We 'd get an inferred intention , we <disfmarker> we produce a structure that differentiates between the Vista , the Enter , and the , um , Tango mode .
speaker: B
text: Which I think we maybe want to ignore .
speaker: B
text: But . That 's my idea .
speaker: B
text: It 's up for discussion .
speaker: B
text: We can change all of it ,
speaker: B
text: any bit of it .
speaker: B
text: Throw it all away .
speaker: F
text: Now @ @ this email that you sent , actually .
speaker: C
text: What ?
speaker: F
text: Now I remember the email .
speaker: C
text: OK .
speaker: E
text: Huh .
speaker: E
text: Still , I have no recollection whatsoever of the email .
speaker: E
text: I 'll have to go back and check .
speaker: C
text: Not important .
speaker: C
text: So , what is important is that we understand what the proposed task is .
speaker: C
text: And , the <disfmarker> the i uh , Robert and I talked about this some on Friday .
speaker: C
text: And we think it 's well - formed .
speaker: C
text: So we think it 's a well - formed , uh , starter task for this , uh , deeper understanding in the tourist domain .
speaker: F
text: So , where exactly is the , uh , deeper understanding being done ?
speaker: F
text: Like I mean , s is it before the Bayes - net ?
speaker: F
text: Is it , uh <disfmarker>
speaker: C
text: Well , it 's the <disfmarker> it 's <disfmarker> it 's always all of it .
speaker: C
text: So , in general it 's always going to be , the answer is , everywhere .
speaker: C
text: Uh , so the notion is that , uh , this isn't real deep .
speaker: C
text: But it 's deep enough that you can distinguish between these th three quite different kinds of , uh , going to see some tourist thing .
speaker: C
text: And , so that 's <disfmarker> that 's the quote " deep " that we 're trying to get at .
speaker: C
text: And , Robert 's point is that the current front - end doesn't give you any way to <disfmarker>
speaker: C
text: Not only doesn't it do it , but it also doesn't give you enough information to do it .
speaker: C
text: 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 .
speaker: C
text: So , uh ,
speaker: C
text: and this is <disfmarker> Bu - I in general it 's gonna be true of any kind of deep understanding ,
speaker: C
text: there 's gonna be contextual things ,
speaker: C
text: there 're gonna be linguistic things ,
speaker: C
text: there 're gonna be discourse things ,
speaker: C
text: and they gotta be combined .
speaker: C
text: And , my idea on how to combine them is with a belief - net ,
speaker: C
text: although it may turn out that t some totally different thing is gonna work better .
speaker: C
text: Um , the idea would be that <vocalsound> you , uh , take your <disfmarker>
speaker: C
text: You 're editing your slide ?
speaker: B
text: Yeah . As i a sort of , as I get ideas ,
speaker: B
text: uh w uh .
speaker: C
text: Oh .
speaker: B
text: So , discourse <disfmarker> I <disfmarker> I <disfmarker> I thought about that .
speaker: B
text: Of course that needs to sort of go in there .
speaker: C
text: Oh . I 'm sorry .
speaker: C
text: OK . So . This is minutes <disfmarker> taking minutes as we go ,
speaker: C
text: in his <disfmarker> in his own way .
speaker: B
text: Yep .
speaker: C
text: Um , but the p the <disfmarker>
speaker: C
text: Anyway . So the thing is , <vocalsound> i uh , d naively speaking , you 've <disfmarker> you 've got a <disfmarker> for this little task , a belief - net ,
speaker: C
text: which is going to have as output , the conditional pr probability of one of three things ,
speaker: C
text: that the person wants to <disfmarker> uh , to View it , to Enter it , or to Tango with it .
speaker: C
text: Um .
speaker: C
text: So that <disfmarker> the <disfmarker> the output of the belief - net is pretty well formed .
speaker: C
text: And , then the inputs are going to be these kinds of things .
speaker: C
text: And , then the question is <disfmarker>
speaker: C
text: there are two questions <disfmarker>
speaker: C
text: is , uh , one , where do you get this i <comment> information from ,
speaker: C
text: and two , what 's the structure of the belief - net ?
speaker: C
text: So what are the conditional probabilities of this , that , and the other , given these things ?
speaker: C
text: And you probably need intermediate nodes .
speaker: C
text: I <disfmarker> we don't know what they are yet .
speaker: C
text: So it may well be that , uh , for example , that , uh , knowing whether <disfmarker>
speaker: C
text: Oh , another thing you want is some information abou I think , about the time of day .
speaker: C
text: Now , they may wanna call that part of context .
speaker: C
text: But the time of day matters a lot .
speaker: B
text: Mm - hmm .
speaker: C
text: And , if things are obviously closed , then , you <disfmarker>
speaker: B
text: People won't want to enter it .
speaker: C
text: Pe - people don't wanna enter them .
speaker: C
text: And , if it 's not obvious , you may want to actually uh , point out to people that it 's closed <disfmarker> you know , what they 're g going to is closed and they don't have the option of entering it .
speaker: B
text: s b
speaker: C
text: 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 <disfmarker> more of a dialogue .
speaker: C
text: So if someone says something you could ask them .
speaker: E
text: Yeah .
speaker: C
text: OK .
speaker: C
text: And <disfmarker> Now , one thing you could do is always ask them ,
speaker: C
text: but that 's boring .
speaker: C
text: And it also w it also be a pain for the person using it .
speaker: C
text: 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 .
speaker: C
text: Ask them which one you want . "
speaker: C
text: OK .
speaker: C
text: But that 's , um , not what we 're gonna do .
speaker: B
text: But maybe that 's a false state of the system , that it 's too close to call .
speaker: C
text: Oh yeah .
speaker: C
text: You want the <disfmarker> you want the ability to a You want the ability to ask ,
speaker: C
text: but what you don't wanna do is onl build a system that always asks every time ,
speaker: C
text: and i
speaker: C
text: That 's not getting at the scientific problem ,
speaker: C
text: and it 's <disfmarker>
speaker: B
text: Mm - hmm .
speaker: C
text: In general you 're <disfmarker> you know , it 's gonna be much more complex than that .
speaker: C
text: a This is purposely a really simple case .
speaker: B
text: Yeah .
speaker: C
text: So , uh <disfmarker>
speaker: C
text: Yeah .
speaker: B
text: I have one more point to <disfmarker> to Bhaskara 's question .
speaker: B
text: Um , I think also the <disfmarker> the <disfmarker> the deep understanding part of it is <disfmarker> is going to be in there to the extent that we um , want it in terms of our modeling .
speaker: B
text: We can start , you know , basic from human beings ,
speaker: B
text: model that ,
speaker: B
text: its motions ,
speaker: B
text: going , walking , seeing ,
speaker: B
text: we can mem model all of that
speaker: B
text: and then compose whatever inferences o we make out of these really conceptual primitives .
speaker: B
text: That will be extremely deep in the <disfmarker> in <disfmarker> in <disfmarker> in my understanding .
speaker: C
text: Yeah . S so <disfmarker> so the way that might come up , if you wanna <disfmarker> Suppose you wanted to do that , you might say , " Um , as an intermediate step in your belief - net , is there a Source - Path - Goal schema involved ? "
speaker: C
text: OK ?
speaker: C
text: And if so , uh , is there a focus on the goal ?
speaker: C
text: Or is there a focus on the path ?
speaker: C
text: or something .
speaker: C
text: And that could be , uh , one of the conditiona
speaker: C
text: you know , th the <disfmarker> In some piece of the belief - net , that could be the <disfmarker> the appropriate thing to enter .
speaker: F
text: So , where would we extract that information from ?
speaker: F
text: From the M - three - L ?
speaker: C
text: No .
speaker: C
text: No . See , the M - three - L is not gonna give th
speaker: C
text: What he was saying is , the M - three - L does not have any of that .
speaker: C
text: All it has is some really crude stuff saying , " A person wants to go to a place . "
speaker: F
text: Right .
speaker: E
text: The M - three - L is the old SmartKom output ?
speaker: C
text: Right .
speaker: E
text: OK .
speaker: C
text: M - three well , M - three - L itself refers to Multimedia Mark - up Language .
speaker: E
text: It 's just a language .
speaker: E
text: Right , yeah .
speaker: C
text: So we have th w we we we have to have a better w way of referring to <disfmarker>
speaker: B
text: The parser output ?
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah .
speaker: B
text: " Analyzed speech " I think it 's what they call it ,
speaker: C
text: The <disfmarker>
speaker: C
text: Well , OK .
speaker: B
text: really , oder <disfmarker>
speaker: C
text: Yeah .
speaker: B
text: o th No , actually , intention lattices is what we 're gonna get .
speaker: C
text: Is - i but they c they call it intention lattice ,
speaker: C
text: but tha
speaker: B
text: In - in a intention lattice k Hypothesis .
speaker: C
text: Anyway .
speaker: B
text: They call it intention hypotheses .
speaker: C
text: Right .
speaker: C
text: So , th they 're gonna give us some cr uh <disfmarker> or <disfmarker> We can assume that y you get this crude information .
speaker: C
text: About intention ,
speaker: C
text: and that 's all they 're going to provide .
speaker: C
text: And they don't give you the kind of object ,
speaker: C
text: they don't give you any discourse history ,
speaker: C
text: if you want to keep that you have to keep it somewhere else .
speaker: B
text: Well , they keep it .
speaker: B
text: We have to request it .
speaker: C
text: Right .
speaker: B
text: Nuh ?
speaker: B
text: But it 's not in there .
speaker: C
text: Well , they <disfmarker> they kee they keep it by their lights .
speaker: C
text: It may <disfmarker> it may or may not be what <disfmarker> what we want .
speaker: B
text: Hmm .
speaker: B
text: Yeah , or i
speaker: C
text: Yeah .
speaker: E
text: So , if someone says , " I wanna touch the side of the Powder - Tower " , that would <disfmarker> basically , we need to pop up Tango mode and the <disfmarker> and the directions ?
speaker: C
text: If i if <disfmarker> Yeah , if it got as simple as that , yeah .
speaker: E
text: Yeah .
speaker: C
text: But it wouldn't .
speaker: E
text: OK .
speaker: E
text: But that doesn't necessarily <disfmarker>
speaker: E
text: But we 'd have to infer a Source - Path - Goal to some degree for touching the side , right ?
speaker: B
text: Well <disfmarker> Uh , th the there is a p a point there if I understand you . Correct ?
speaker: B
text: Um ,
speaker: B
text: because um , sometimes people just say things <disfmarker>
speaker: B
text: This you find very often .
speaker: B
text: " Where is the city hall ? "
speaker: B
text: And this do they don't wanna sh see it on a map ,
speaker: B
text: or they don't wanna know it 's five hundred yards away from you , or that it 's to the <disfmarker> your north .
speaker: B
text: They wanna go there .
speaker: B
text: That 's what they say , is , " Where is it ? " .
speaker: B
text: Where is that damn thing ?
speaker: E
text: And the parser would output <disfmarker>
speaker: B
text: Well , that 's a <disfmarker> a question mark .
speaker: B
text: sh
speaker: B
text: A lot of parsers , um ,
speaker: B
text: just , uh <disfmarker>
speaker: B
text: That 's way beyond their scope , is <disfmarker> of interpreting that .
speaker: B
text: You know ?
speaker: B
text: But um ,
speaker: B
text: still outcome w the outcome will be some form of structure ,
speaker: B
text: with the town hall and maybe saying it 's a WH focus on the town hall .
speaker: B
text: But to interpret it ,
speaker: D
text: Mm - hmm .
speaker: B
text: you know ?
speaker: B
text: somebody else has to do that job later .
speaker: E
text: I 'm just trying to figure out what the SmartKom system would output ,
speaker: C
text: Yeah .
speaker: E
text: depending on these things .
speaker: B
text: Um , it will probably tell you how far away it is ,
speaker: B
text: at least that 's <disfmarker> That 's even what Deep Map does .
speaker: B
text: It tells you how far away it is ,
speaker: B
text: and <disfmarker> and shows it to you on a map .
speaker: B
text: 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 <disfmarker> where it is .
speaker: D
text: People no might not be able to infer that either , right ?
speaker: D
text: Like the fact <disfmarker> 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 ? "
speaker: D
text: Because how I describe um , t its location <disfmarker> uh , 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 .
speaker: B
text: Mm - hmm .
speaker: B
text: It 's a granularity factor ,
speaker: C
text: Yeah .
speaker: B
text: because where people ask you , " Where is New York ? " , you will tell them it 's on the East Coast .
speaker: D
text: Uh - huh .
speaker: D
text: Yeah . Exactly .
speaker: D
text: Right .
speaker: B
text: Y y eh <disfmarker> you won't tell them how to get there , ft you know , take that bus to the airport and blah - blah - blah .
speaker: D
text: Right .
speaker: D
text: Yeah .
speaker: D
text: Right .
speaker: B
text: But if it 's the post office , you will tell them how to get there .
speaker: D
text: Mm - hmm .
speaker: B
text: So th They have done some interesting experiments on that in Hamburg as well .
speaker: D
text: Right .
speaker: D
text: Right .
speaker: B
text: So .
speaker: C
text: But <disfmarker>
speaker: C
text: i Go <disfmarker> go back to the <disfmarker> the uh , th
speaker: C
text: Yeah , that slide .
speaker: B
text: So I w this is <disfmarker> " onto " is <disfmarker> is knowledge about buildings ,
speaker: B
text: their opening times ,
speaker: B
text: and then t coupled with time of day , um , this should <disfmarker>
speaker: B
text: You know .
speaker: D
text: So that context was like , um , their presumed purpose context ,
speaker: D
text: i like business or travel ,
speaker: D
text: as well as the utterance context , like , " I 'm now standing at this place at this time " .
speaker: C
text: Yeah , well I think we ought to d a As we have all along , d We <disfmarker> we 've been distu distinguishing between situational context ,
speaker: C
text: which is what you have as context ,
speaker: C
text: and discourse context ,
speaker: B
text: Mm - hmm .
speaker: C
text: which you have as DH ,
speaker: C
text: I don't know what the H means .
speaker: B
text: Nuh .
speaker: B
text: History .
speaker: B
text: Discourse history .
speaker: C
text: OK .
speaker: C
text: Whatever .
speaker: B
text: Yeah .
speaker: C
text: So we can work out terminology later .
speaker: B
text: Yep .
speaker: C
text: So , they 're <disfmarker> they 're quite distinct .
speaker: C
text: I mean , you need them both ,
speaker: C
text: but they 're quite distinct .
speaker: C
text: And , so what we were talking about doing , a a as a first shot , is not doing any of the linguistics .
speaker: C
text: Except to find out what seems to be <pause> useful .
speaker: C
text: So , the <disfmarker> the <disfmarker> the reason the belief - net is in blue , is the notion would be <disfmarker>
speaker: C
text: Uh , this may be a bad dis bad idea ,
speaker: C
text: 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 uh , in a plausible way ,
speaker: C
text: given
speaker: C
text: these <disfmarker> We have all these transcripts
speaker: C
text: and we 're able to , by hand , extract the features to put in the belief - net .
speaker: C
text: Saying , " Aha !
speaker: C
text: here 're the things which , if you get them out of <disfmarker> out of the language and discourse , and put them into the belief - net , it would tell you which of these three uh , intentions is most likely . "
speaker: C
text: And if <disfmarker> to actually do that , build it , um <disfmarker> you know , run it <disfmarker> y y run it on the data where you hand - transcribe the parameters .
speaker: C
text: And see how that goes .
speaker: C
text: If that goes well , then we can start worrying about how we would extract them .
speaker: C
text: So <disfmarker> where would you get this information ?
speaker: C
text: And , expand it to <disfmarker> to other things like this .
speaker: C
text: But if we can't do that , then we 're in trouble .
speaker: C
text: I mean th th i i if you can't do this task ,
speaker: C
text: um <disfmarker>
speaker: B
text: We need a different , uh , engine .
speaker: C
text: Uh , uh , yeah , or something .
speaker: B
text: Machine , I mean .
speaker: C
text: Well it <disfmarker> i I if it <disfmarker> if it 's the belief - nets , we we 'll switch to you know , logic or some terrible thing ,
speaker: C
text: but I don't think that 's gonna be the case .
speaker: C
text: I think that , uh , if we can get the information , a belief - net is a perfectly good way of doing the inferential combination of it .
speaker: C
text: The real issue is , do what are the factors involved in determining this ?
speaker: C
text: And I don't know .
speaker: B
text: Hmm .
speaker: B
text: But , only w
speaker: C
text: Hold on a s Hold on a second .
speaker: B
text: Muh .
speaker: C
text: So , I know .
speaker: C
text: Uh , uh , is it clear what 's going on here ?
speaker: F
text: Yep .
speaker: D
text: Um , I missed the beginning ,
speaker: D
text: but , um
speaker: D
text: I guess <disfmarker>
speaker: D
text: could you back to the slide ,
speaker: D
text: the previous one ?
speaker: D
text: So , is it that it 's , um <disfmarker>
speaker: D
text: These are all factors that uh , a These are the ones that you said that we are going to ignore now ?
speaker: D
text: or that we want to <vocalsound> take into account ?
speaker: D
text: You were saying n
speaker: C
text: Take them into account .
speaker: D
text: Take the <disfmarker> the linguistic factors too .
speaker: C
text: But <disfmarker> but you don't worry about <disfmarker> h
speaker: D
text: Oh , how to extract these features .
speaker: C
text: how to extract them .
speaker: D
text: OK .
speaker: C
text: So , f let 's find out which ones we need first ,
speaker: D
text: Got it .
speaker: D
text: OK .
speaker: D
text: And <disfmarker> and it 's clear from the data , um , like , sorta the correct answer in each case .
speaker: C
text: and <disfmarker>
speaker: C
text: No .
speaker: D
text: But l
speaker: D
text: OK .
speaker: B
text: No .
speaker: D
text: That 's <disfmarker> that 's the thing I 'm curious ab
speaker: B
text: But <disfmarker>
speaker: C
text: Let 's go back to th Let 's go back to the <disfmarker> the <disfmarker> the slide of data .
speaker: D
text: Like do we know from the data wh which <disfmarker>
speaker: B
text: Um <disfmarker>
speaker: D
text: OK .
speaker: D
text: So <disfmarker>
speaker: B
text: Not from that data .
speaker: B
text: But , um , since we are designing a <disfmarker> a <disfmarker> a <disfmarker> an , compared to this , even bigger data collection effort , <comment> um , we will definitely take care to put it in there ,
speaker: D
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: B
text: in some shape , way , form over the other ,
speaker: C
text: Yeah .
speaker: B
text: to see whether we can , then , get sort of empirically validated data .
speaker: D
text: Right .
speaker: B
text: Um , from this , we can sometimes , you know <disfmarker>
speaker: B
text: an and that 's <disfmarker> that <disfmarker> but that <disfmarker> isn't that what we need for a belief - net anyhow ?
speaker: B
text: is sort of <disfmarker>
speaker: B
text: s sometimes when people want to just see it , they phrase it more like this ?
speaker: D
text: Mm - hmm .
speaker: B
text: But it doesn't exclude anybody from phrasing it totally differently , even if they still <disfmarker>
speaker: D
text: Right .
speaker: D
text: Right .
speaker: B
text: you know ?
speaker: B
text: But then other factors may come into play that change the outcome of their belief - net .
speaker: D
text: Right .
speaker: B
text: So , um , this is exactly what <disfmarker>
speaker: B
text: Because y you can never be sure .
speaker: B
text: 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 . "
speaker: D
text: Sure .
speaker: B
text: You know , because then , uh , you <disfmarker>
speaker: D
text: u u I mean , the only way you could get that is if you were to give th the x subjects a task .
speaker: D
text: Right ?
speaker: D
text: Where you have <disfmarker> where your , uh , current goal is to <disfmarker>
speaker: B
text: We
speaker: B
text: Yeah !
speaker: B
text: That 's what we 're doing .
speaker: D
text: So that 's what you want ?
speaker: B
text: But <disfmarker> but we will still get the phrasing all over the place .
speaker: D
text: OK .
speaker: D
text: So you will know .
speaker: D
text: Mm - hmm .
speaker: B
text: I 'm sure that , you know <disfmarker>
speaker: C
text: Yeah .
speaker: D
text: The <disfmarker> No , that 's fine .
speaker: D
text: I guess , it 's just knowing the intention from the experimental subject .
speaker: C
text: Yeah .
speaker: B
text: Mm - hmm .
speaker: C
text: From that task , yeah .
speaker: C
text: So , uh , I think you all know this , but we are going to actually use this little room
speaker: C
text: and start recording subjects probably within a month or something .
speaker: C
text: So , this is not any <disfmarker> lo any of you guys ' worry ,
speaker: C
text: except that we may want to push that effort to get information we need .
speaker: C
text: So our job <vocalsound> is to figure out how to solve these problems .
speaker: C
text: If it turns out that we need data of a certain sort , then the sort of data collection branch can be , uh , asked to do that .
speaker: C
text: 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 uh , recording of subjects .
speaker: C
text: So , yeah <disfmarker> y you 're absolutely right , though .
speaker: C
text: No , you <disfmarker> you will not have ,
speaker: C
text: and there it is ,
speaker: C
text: and , uh <disfmarker>
speaker: C
text: But you know , y y
speaker: C
text: the , um <disfmarker>
speaker: D
text: And I think the other concern that has come up before , too , is if it 's <disfmarker>
speaker: D
text: um <disfmarker>
speaker: D
text: I don't know if this was collected <disfmarker> what situation this data was collected in .
speaker: D
text: Was it <disfmarker> is it the one that you showed in your talk ?
speaker: D
text: Like people <disfmarker>
speaker: B
text: No , no .
speaker: D
text: But
speaker: B
text: No .
speaker: D
text: OK .
speaker: D
text: So was this , like , someone actually mobile , like <disfmarker> s using a device ?
speaker: B
text: Uh ,
speaker: B
text: N no , no not <disfmarker>
speaker: B
text: i it was mobile
speaker: B
text: but not <disfmarker> not with a w a real wizard system .
speaker: B
text: So there were never answers .
speaker: D
text: Uh - huh .
speaker: D
text: OK . OK .
speaker: D
text: But , is it <disfmarker>
speaker: D
text: I guess I don't know <disfmarker> The situation of <disfmarker> of collecting th the data
speaker: D
text: of , like <disfmarker> Here you could imagine them being <disfmarker> walking around the city . as like one situation .
speaker: D
text: 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 .
speaker: B
text: Mm - hmm .
speaker: D
text: If they 're doing it in a <disfmarker>
speaker: D
text: you know , " I 'm sitting here with a map and asking questions " ,
speaker: D
text: I <disfmarker> I would imagine that the data would be really different .
speaker: D
text: Um , so it 's just <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: But <disfmarker> It was never th th the goal of that data collection to <disfmarker> to serve for sat for such a purpose .
speaker: B
text: So that 's why for example the tasks were not differentiated by intentionality ,
speaker: D
text: Mm - hmm .
speaker: B
text: there was n there was no label ,
speaker: D
text: Mm - hmm .
speaker: D
text: Right .
speaker: B
text: you know , intention A , intention B , intention C .
speaker: B
text: Or task A , B , C .
speaker: B
text: Um
speaker: B
text: I 'm sure we can produce some if we need it ,
speaker: D
text: Mm - hmm .
speaker: B
text: um , that <disfmarker> that will help us along those lines .
speaker: B
text: But , you know , you gotta leave something for other people to model .
speaker: B
text: So , to <disfmarker> Finding out what , you know , situational con what the contextual factors of the situation really are , you know is an interesting s interesting thing .
speaker: D
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: B
text: u u Sort of I 'm , at the moment , curious and I 'm <disfmarker> I 'm <disfmarker> 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 ,
speaker: D
text: Mm - hmm .
speaker: B
text: so that we get a clearer notion of what input we need for that ,
speaker: D
text: Mm - hmm .
speaker: B
text: what suffices and what doesn't .
speaker: B
text: And then we can start worrying about where to get this input ,
speaker: B
text: what <disfmarker> what do we need , you know <disfmarker>
speaker: B
text: Ultimately once we are all experts in changing that parser , for example , maybe , there 's just a couple three things we need to do
speaker: B
text: and then we get more whatever , part of speech and more construction - type - like stuff out of it .
speaker: D
text: Mm - hmm .
speaker: D
text: Hmm .
speaker: B
text: It 's a m pragmatic approach , uh , at the moment .
speaker: E
text: How exactly does the data collection work ?
speaker: E
text: Do they have a map ,
speaker: E
text: and then you give them a scenario of some sort ?
speaker: B
text: OK .
speaker: B
text: Imagine you 're the <disfmarker> the subject .
speaker: B
text: You 're gonna be in here ,
speaker: B
text: and somebody <disfmarker>
speaker: B
text: And <disfmarker> and you see , uh , either th the three - D model , or uh , a QuickTime animation of standing u in a square in Heidelberg .
speaker: B
text: So you actually see that .
speaker: B
text: Um . The uh ,
speaker: B
text: um , first thing is you have to read a text about Heidelberg .
speaker: B
text: So , just off a textbook , uh , tourist guide , to familiarize , uh , yourself with that sort of odd - sounding German street names , like Fischergasse and so forth .
speaker: B
text: So that 's part one .
speaker: B
text: Part two is , you 're told that this huge new , wonderful computer system exists , that can y tell you everything you want to know ,
speaker: B
text: and it understands you completely .
speaker: B
text: And so you 're gonna pick up that phone ,
speaker: B
text: dial a number ,
speaker: B
text: and you get a certain amount of tasks that you have to solve .
speaker: B
text: First you have to know <disfmarker> find out how to get to that place ,
speaker: B
text: maybe with the intention of buying stamps in there .
speaker: B
text: Maybe <disfmarker> So , the next task is to get to a certain place and take a picture for your grandchild .
speaker: B
text: The third one is to get information on the history of an object .
speaker: B
text: The fourth one <disfmarker>
speaker: B
text: And then the g system breaks down .
speaker: B
text: It crashes ,
speaker: D
text: a At the third ?
speaker: B
text: And <disfmarker>
speaker: D
text: Right then ?
speaker: B
text: After the third task .
speaker: D
text: OK .
speaker: B
text: And then <disfmarker>
speaker: B
text: Or after the fourth .
speaker: B
text: Some find <disfmarker> @ @ <comment> Forget that for now .
speaker: B
text: And then , a human operator comes on , and <disfmarker> and exp apologizes that the system has crashed ,
speaker: B
text: but , you know , urges you to continue , you know ?
speaker: B
text: now with a human operator .
speaker: B
text: And so , you have basically the same tasks again ,
speaker: B
text: just with different objects ,
speaker: B
text: and you go through it again ,
speaker: B
text: and that was it .
speaker: B
text: Oh , and one <disfmarker> one little bit <disfmarker>
speaker: B
text: w And uh , the computer you are <disfmarker> you are being told the computer system knows exactly where you are , via GPS .
speaker: B
text: When the human operator comes on , um , that person does not know .
speaker: B
text: So the GPS is crashed as well .
speaker: B
text: So the person first has to ask you " Where are you ? " .
speaker: B
text: And so you have to do some <disfmarker> s tell the person sort of where you are , depending on what you see there .
speaker: B
text: Um , this is a <disfmarker> a <disfmarker> a <disfmarker> a <disfmarker> a bit that I d I don't think we <disfmarker>
speaker: B
text: Did we discuss that bit ?
speaker: B
text: Uh , I just sort of squeezed that in now .
speaker: B
text: But it 's something , uh , that would provide some very interesting data for some people I know .
speaker: B
text: So .
speaker: D
text: So , in the display you can <disfmarker> Oh , you said that you cou you might have a display that shows , like , the <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: a Additionally , y you have a <disfmarker> a <disfmarker> a sort of a map type display .
speaker: D
text: a w your perspective ? sort of ?
speaker: D
text: And so , as you <disfmarker>
speaker: B
text: Uh , two - D .
speaker: B
text: n
speaker: D
text: Oh , two - D .
speaker: D
text: OK .
speaker: B
text: Two - D .
speaker: D
text: So as you move through it that 's - they just track it on the <disfmarker> for themselves
speaker: B
text: Yeah .
speaker: B
text: b y
speaker: B
text: You don't <disfmarker>
speaker: D
text: there .
speaker: B
text: That 's <disfmarker>
speaker: B
text: I don't know .
speaker: B
text: I but y I don't think you really move ,
speaker: D
text: OK .
speaker: B
text: sort of .
speaker: D
text: So
speaker: B
text: Yeah ?
speaker: B
text: I mean that would be an <disfmarker> an <disfmarker> an enormous technical effort ,
speaker: B
text: unless we would <disfmarker>
speaker: B
text: We can show it walks to , you know .
speaker: B
text: We can have movies of walking ,
speaker: B
text: you walking through <disfmarker> through Heidelberg , and u ultimately arriving there .
speaker: D
text: Mm - hmm .
speaker: B
text: Maybe we wanna do that .
speaker: B
text: Yeah .
speaker: D
text: Uh , I was just trying to figure out how <disfmarker> how ambitious the system is .
speaker: B
text: The map was sort of intended to <disfmarker>
speaker: B
text: You want to go to that place .
speaker: D
text: Mm - hmm .
speaker: B
text: You know , and it 's sort of there .
speaker: B
text: And you see the label of the name <disfmarker>
speaker: B
text: So we get those names , pronunciation stuff , and so forth ,
speaker: D
text: Mm - hmm .
speaker: B
text: and we can change that .
speaker: D
text: Mm - hmm .
speaker: D
text: So your tasks don't require you to <disfmarker>
speaker: D
text: I mean , uh <disfmarker> yo you 're told <disfmarker> So when your task is , I don't know , " Go buy stamps " or something like that ?
speaker: D
text: So , do you have to respond ?
speaker: D
text: or does your <disfmarker>
speaker: D
text: Uh , what are you ste what are you supposed to be telling the system ?
speaker: D
text: Like , w
speaker: D
text: what you 're doing now ?
speaker: D
text: or <disfmarker>
speaker: B
text: Well , we 'll see what people do .
speaker: D
text: There 's no <disfmarker>
speaker: D
text: OK , so it 's just like , " Let 's figure out what they would say under the circumstances " .
speaker: B
text: Yeah , and <disfmarker> and we will record both sides .
speaker: B
text: I mean , we will record the Wi - the Wizard <disfmarker>
speaker: D
text: Uh - huh .
speaker: D
text: Uh - huh .
speaker: B
text: I mean , in both cases it 's gonna be a human , in the computer , and in the operator case .
speaker: B
text: And we will re there will be some dialogue , you know ?
speaker: B
text: So , you first have to do this , and that ,
speaker: D
text: Yep .
speaker: B
text: and <disfmarker> and <disfmarker>
speaker: D
text: Mm - hmm .
speaker: B
text: see wh what they say .
speaker: B
text: We can ins instruct the , uh , wizard in how expressive and talkative he should be .
speaker: B
text: But um ,
speaker: B
text: maybe the <disfmarker> maybe what you 're suggesting <disfmarker>
speaker: B
text: 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 uh , task results in a question and then there 's an answer and that 's the end of the task ?
speaker: B
text: You wanna m have it more <disfmarker> more steps , sort of ?
speaker: D
text: Yeah , I <disfmarker> I don't know how much direction is given to the subject about what their interaction <disfmarker>
speaker: D
text: I mean , th they 're unfamiliar w with interacting with the system .
speaker: D
text: All they know is it 's this great system that could do s stuff .
speaker: B
text: Mm - hmm .
speaker: B
text: Mm - hmm .
speaker: C
text: Oh yeah , but <disfmarker> to some extent this is a different discussion .
speaker: D
text: Right ?
speaker: D
text: So <disfmarker>
speaker: C
text: OK ?
speaker: C
text: So . Uh , we <disfmarker> we have to have this discussion of th the experiment , and the data collection , and all that sorta stuff
speaker: D
text: Uh - huh .
speaker: C
text: and we do have , um , a student who is a candidate for wizard .
speaker: C
text: Uh , she 's gonna get in touch with me .
speaker: C
text: It 's a student of Eve 's .
speaker: C
text: FEY ,
speaker: C
text: Fey ?
speaker: C
text: Spelled FEY .
speaker: C
text: Do you <disfmarker> do you <disfmarker>
speaker: D
text: Oh , Fey Parrill .
speaker: C
text: You know her ?
speaker: D
text: Yeah .
speaker: D
text: Uh - huh .
speaker: C
text: OK .
speaker: C
text: Sh - Is sh
speaker: D
text: She started taking the class last year
speaker: D
text: and then didn't <disfmarker> um , you know , didn't continue .
speaker: D
text: I g She 's a g
speaker: D
text: Is she an undergradua
speaker: C
text: She 's graduated .
speaker: D
text: She is a graduate , OK .
speaker: C
text: Yeah .
speaker: D
text: Yeah , I m I know her very , very briefly .
speaker: D
text: I know she was inter you know , interested in aspect and stuff like that .
speaker: C
text: OK .
speaker: C
text: So , anyway , she 's looking for some more part time work w while she 's waiting actually for graduate school .
speaker: C
text: And she 'll be in touch .
speaker: C
text: So we may have someone , uh , to do this ,
speaker: C
text: and she 's got you know , some background in <disfmarker> in all this stuff .
speaker: C
text: And is a linguist st
speaker: C
text: and , so
speaker: C
text: So . <vocalsound> That 's <disfmarker> 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 .
speaker: D
text: Mm - hmm .
speaker: C
text: And um , Jane , but also , uh , Liz have offered to help us do this , uh , data collection and design and stuff .
speaker: D
text: Mm - hmm .
speaker: D
text: Mmm .
speaker: C
text: So , when we get to that we 'll have some people doing it that know what they 're doing .
speaker: D
text: OK .
speaker: D
text: I guess the reason I was asking about the sort of the de the details of this kind of thing is that , um , 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 ,
speaker: D
text: but with intention <vocalsound> um , obviously , as you point out , there 's a lot of di other factors
speaker: D
text: and <disfmarker> I 'm not really sure , um , how <disfmarker> how <disfmarker> e the question of how to make it a t appropriate toy version of that <disfmarker> Um , it 's ju it 's just hard .
speaker: D
text: So , I mean , obviously it 's a <disfmarker>
speaker: E
text: Yeah , uh , actually I guess that was my question .
speaker: E
text: Is the intention implicit in the scenario that 's given ?
speaker: E
text: Like , do the <disfmarker>
speaker: D
text: It is , if they have these tasks that they 're supposed to <disfmarker>
speaker: E
text: Yeah , I just wasn't sure to what level of detail the task was .
speaker: D
text: to <disfmarker> to give <disfmarker>
speaker: D
text: Yeah ,
speaker: D
text: uh <disfmarker>
speaker: B
text: Mm - hmm .
speaker: D
text: Right .
speaker: B
text: n No one is , at the moment .
speaker: D
text: Right .
speaker: E
text: OK .
speaker: C
text: So , we that 's part of what we 'll have to figure out .
speaker: D
text: Right .
speaker: D
text: Mm - hmm .
speaker: C
text: But , uh ,
speaker: C
text: the <disfmarker> 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 ,
speaker: C
text: and set the context ,
speaker: C
text: and know what language was used .
speaker: C
text: So let 's suppose that we can get that kind of data .
speaker: C
text: Um .
speaker: C
text: The issue is , can we find a way to , basically , featurize it
speaker: C
text: 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 .
speaker: D
text: w What are the t three intentions ?
speaker: D
text: Is it to go there , to see it , and <disfmarker>
speaker: B
text: To come as close as possible to it .
speaker: C
text: Th - the terminology we 're using is to <disfmarker>
speaker: D
text: Yeah , it 's @ @ .
speaker: C
text: Go back .
speaker: C
text: To v
speaker: D
text: OK .
speaker: C
text: to View it .
speaker: C
text: OK ?
speaker: C
text: To Enter it .
speaker: C
text: Now those <disfmarker> It seems to me those are cl you c you have no trouble with those being distinct .
speaker: C
text: " Take a picture of it "
speaker: D
text: Mm - hmm .
speaker: C
text: you <disfmarker> you might well want to be a really rather different place than entering it .
speaker: D
text: Mm - hmm .
speaker: C
text: 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 .
speaker: D
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: C
text: Just sort of <disfmarker>
speaker: D
text: OK , so now I understand the referent of Tango mode .
speaker: E
text: See , I would have thought it was more of a waltz .
speaker: D
text: I didn't get that before .
speaker: B
text: S
speaker: B
text: To " Waltz " it ?
speaker: D
text: Yeah , like , how close are you gonna be ?
speaker: C
text: Well .
speaker: D
text: Like , <vocalsound> Tango 's really close .
speaker: E
text: Yeah , cuz a tango <disfmarker>
speaker: E
text: Yeah .
speaker: C
text: Well , anyway .
speaker: F
text: All these
speaker: C
text: So <disfmarker>
speaker: F
text: So , like , the question is how what features can <disfmarker> like , do you wanna try to extract from , say , the parse or whatever ?
speaker: C
text: Right .
speaker: F
text: Like , the presence of a word or the presence of a certain uh , stem , or <disfmarker> certain construction or whatever .
speaker: C
text: Right .
speaker: C
text: Is there a construction , or the kind of object ,
speaker: C
text: or w uh , anything else that 's in the si It 's either in the <disfmarker> in the s the discourse itself or in the context .
speaker: C
text: 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 .
speaker: C
text: Now , how you determine that is another issue .
speaker: C
text: 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
speaker: C
text: and then just have a little section in your belief - net that said , " pppt ! "
speaker: C
text: Though sin f in the short run , you 'd set them ,
speaker: F
text: Mm - hmm .
speaker: C
text: and see ho how it worked ,
speaker: C
text: 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 .
speaker: F
text: Right .
speaker: F
text: So , how should <disfmarker> What 's the uh , plan ?
speaker: F
text: Like , how should we go about figuring out these <disfmarker>
speaker: C
text: OK . So , first of all is ,
speaker: C
text: uh , do e either of you guys , you got a favorite belief - net that you 've , you know , played with ?
speaker: C
text: JavaBayes or something ?
speaker: F
text: Oh .
speaker: F
text: No , not really .
speaker: C
text: OK .
speaker: C
text: Well , anyway . f Get one .
speaker: C
text: OK ? So <disfmarker> y so one of th one of the things we wanna do is actually , uh , pick a package ,
speaker: C
text: doesn't matter which one ,
speaker: C
text: uh , presumably one that 's got good interactive abilities ,
speaker: C
text: cuz a lot of what we 're gonna be d
speaker: C
text: You know , we don't need the one that 'll solve massive , uh , belief - nets quickly .
speaker: C
text: d w
speaker: C
text: These are not gonna get big in <disfmarker> in the foreseeable future .
speaker: C
text: But we do want one in which it 's easy to interact with and , uh , modify .
speaker: C
text: Because i that 's <disfmarker> A lot of what it 's gonna be , is , um , playing with this .
speaker: C
text: And probably one in which it 's easy to have , um , what amounts to transcript files .
speaker: C
text: So that if <disfmarker> if we have all these cases <disfmarker>
speaker: C
text: OK ?
speaker: C
text: So we make up cases that have these features ,
speaker: C
text: OK , and then you 'd like to be able to say , " OK , here 's a bunch of cases " <disfmarker>
speaker: C
text: There 're even ones tha that you can do learning
speaker: C
text: OK ? So you have all their cases and <disfmarker> and their results
speaker: C
text: and you have a <disfmarker> algorithms to go through and run around trying to set the <disfmarker> the probabilities for you .
speaker: C
text: Um ,
speaker: C
text: probably that 's not worth it .
speaker: C
text: I mean , my guess is we aren't gonna have enough data that 's good enough to make the <disfmarker> these data fitting ones worth it ,
speaker: C
text: but I don't know .
speaker: C
text: So I would say you guy the first task for you two guys is to um , pick a package .
speaker: C
text: OK , and you wanna it s You know , the standard things
speaker: C
text: you want it stable , you want it <disfmarker>
speaker: C
text: yeah , @ @ .
speaker: C
text: And , as soon as we have one , we can start trying to , uh , make a first cut at what 's going on .
speaker: B
text: An - Nuh .
speaker: C
text: But it <disfmarker> what I like about it is it 's very concrete .
speaker: C
text: OK ? We <disfmarker> we have a <disfmarker> we know what the outcomes are gonna be ,
speaker: C
text: and we have some <disfmarker> some data that 's loose ,
speaker: C
text: we can use our own intuition ,
speaker: C
text: and see how hard it is ,
speaker: C
text: and , importantly , what intermediate nodes we think we need .
speaker: C
text: So it <disfmarker> if it turns out that just , thinking about the problem , you come up with things you really need to <disfmarker> 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 .
speaker: E
text: Mm - hmm .
speaker: B
text: And it <disfmarker> and it may serve as a platform for a person , maybe me , or whoever , who is interested in doing some linguistic analysis .
speaker: B
text: I mean , w we have the For - FrameNet group here ,
speaker: B
text: and we can see what they have found out about those concepts already ,
speaker: B
text: that are contained in the data ,
speaker: B
text: um , you know , to come up with a nice little set of features
speaker: B
text: and um , maybe even means of s uh , extracting them .
speaker: B
text: And <disfmarker> and that altogether could also be <disfmarker> uh , become a nice paper that 's going to be published somewhere , if we sit down and write it .
speaker: B
text: And um <disfmarker>
speaker: B
text: When you said JavaBayes belief - net you were talking about ones that run on coffee ?
speaker: B
text: or that are in the program language Java ?
speaker: C
text: No , th It turns out that there is a , uh <disfmarker> The new end of Java libraries .
speaker: C
text: OK , and it turns out one called
speaker: C
text: Which is one that fair <disfmarker> people around here use a fair amount .
speaker: B
text: Mmm .
speaker: B
text: OK .
speaker: C
text: I have no idea whether that 's <disfmarker>
speaker: C
text: 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 .
speaker: B
text: Mm - hmm .
speaker: C
text: So that i that 's I think why a lot of people doing research use that .
speaker: C
text: But it may not be <disfmarker>
speaker: C
text: I have no idea whether that 's the best choice
speaker: C
text: an and there 're plenty of people around , students in the department who , you know , live and breathe Bayes - nets .
speaker: C
text: So , uh ,
speaker: D
text: There 's the m tool kit that um , Kevin Murphy has developed ,
speaker: C
text: Right .
speaker: C
text: It 's OK .
speaker: D
text: which might be useful too .
speaker: F
text: Right .
speaker: C
text: So , yeah , Kevin would be a good person to start with .
speaker: D
text: And it 's available Matlab code .
speaker: C
text: Nancy knows him well .
speaker: C
text: I don't know I don't know whether you guys have met Kevin yet or not ,
speaker: B
text: Mm - hmm .
speaker: C
text: but , uh <disfmarker>
speaker: F
text: Yeah , I know him .
speaker: B
text: But i
speaker: C
text: Yeah .
speaker: B
text: But since we all probably are pretty sure that , um , the <disfmarker>
speaker: B
text: For example , this th th the dialogue history is <disfmarker> is um , producing XML documents .
speaker: B
text: M - three - L of course is XML .
speaker: B
text: And the ontology that um , uh the student is <disfmarker> is constructing for me back in <disfmarker> in EML is in OIL and that 's also in XML .
speaker: B
text: And so that 's where a lot of knowledge about bakeries , about hotels , about castles and stuff is gonna come from .
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah .
speaker: B
text: Um , so , if it has that IO capability and if it 's a Java package , it will definitely be able <disfmarker> We can couple .
speaker: C
text: Yeah .
speaker: C
text: So , yeah , we 're sort of <nonvocalsound> committed to XML as the kind of , uh , interchange .
speaker: C
text: But that 's , you know , not a big deal .
speaker: B
text: Who isn't ,
speaker: B
text: nuh ?
speaker: C
text: So , in terms of <disfmarker> of <disfmarker> interchanging in and out of any module we build , It 'll be XML .
speaker: C
text: And if you 're going off to queries to the ontology , for example , you 'll have to deal with its interface .
speaker: C
text: But that 's <disfmarker> that 's fine
speaker: C
text: an and um ,
speaker: C
text: all of these things have been built with much bigger projects than this in mind .
speaker: C
text: So they <disfmarker> they have worked very hard .
speaker: C
text: It 's kind of blackboards and multi - wave blackboards
speaker: C
text: and ways of interchanging and registering your
speaker: C
text: a And so forth .
speaker: C
text: So ,
speaker: C
text: that I don't think is even worth us worrying about just yet .
speaker: C
text: 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 .
speaker: C
text: I believe .
speaker: C
text: I don't <disfmarker> I don't see <disfmarker>
speaker: B
text: Hmm .
speaker: B
text: Yeah . Yeah , I like , for example , the <disfmarker> what you said about the getting input from <disfmarker> from just files about where you h where you have the data , have specified the features and so forth .
speaker: B
text: That 's , of course , easy also to do with , you know , XML .
speaker: C
text: Uh , you could have an X <disfmarker> yeah , you could make and XML format for that . Sure .
speaker: B
text: So r
speaker: C
text: That <disfmarker> that <disfmarker>
speaker: C
text: um ,
speaker: C
text: you know , feature value XML format is probably as good a way as any .
speaker: C
text: 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 .
speaker: F
text: Doesn't <disfmarker> does SmartKom system have such packages ?
speaker: B
text: Yeah .
speaker: C
text: Sure .
speaker: B
text: The <disfmarker> the lib M - three - L library does that .
speaker: C
text: And the question is , d you c you <disfmarker> you 'll have to l We 'll have to l
speaker: B
text: It 's also <disfmarker>
speaker: C
text: That should be <disfmarker> ay We should be able to look at that <disfmarker>
speaker: B
text: No ,
speaker: B
text: u u y um
speaker: B
text: the <disfmarker>
speaker: B
text: What I <disfmarker> What sort of came to my mind i is <disfmarker> was the notion of an idea that if <disfmarker> if there are l nets that can actually lear try to set their own , um , probability factors based on <disfmarker> on <disfmarker> on <disfmarker> on input <disfmarker>
speaker: C
text: Yeah .
speaker: B
text: which is in file format ,
speaker: B
text: if we , um , get really w wild on this , we may actually want to use some <disfmarker> some corpora that other people made
speaker: B
text: and , for example , if <disfmarker> if they are in <disfmarker> in MATE , then we get X M L documents with discourse annotations , t
speaker: F
text: Mm - hmm .
speaker: B
text: you know , t from the discourse act down to the phonetic level .
speaker: B
text: Um , Michael has a project where <disfmarker> you know , recognizing discourse acts
speaker: B
text: and he does it all in MATE ,
speaker: B
text: and so they 're actually annotating data and data and data .
speaker: B
text: So if we w if we think it 's worth it one of these days , not <disfmarker> not with this first prototype but maybe with a second , and we have the possibility of <disfmarker> of taking input that 's generated elsewhere and learn from that , that 'd be nice .
speaker: F
text: Right .
speaker: C
text: It 'd be nice , but <disfmarker> but I <disfmarker> I <disfmarker> I do I don't wanna count on it .
speaker: C
text: I mean , you can't <disfmarker> you can't run your project based on the speculation that <disfmarker> that the data will come ,
speaker: B
text: No , no , uh , just for <disfmarker>
speaker: C
text: and you don't have to actually design the nets .
speaker: B
text: Nuh .
speaker: B
text: Just a back door that <disfmarker> I <disfmarker> I think we should devote m
speaker: C
text: Could happen .
speaker: C
text: Yeah . So in terms of <disfmarker> of the , um <disfmarker> the <disfmarker> what the SmartKom gives us for M - three - L packages , it could be that they 're fine , or it could be eeh .
speaker: C
text: You don't <disfmarker> You know , you don't really like it .
speaker: C
text: So we 're not <disfmarker> we 're not abs we 're not required to use their packages .
speaker: C
text: We are required at the end to give them stuff in their format ,
speaker: C
text: but hey .
speaker: F
text: Right .
speaker: C
text: Um ,
speaker: C
text: it 's , uh <disfmarker>
speaker: C
text: It doesn't control what you do in you know , internally .
speaker: E
text: What 's the time frame for this ?
speaker: B
text: Two days ?
speaker: C
text: Huh ?
speaker: B
text: Two , three days ?
speaker: C
text: Yeah bu w I 'd like that this <disfmarker> y yeah , this week , to ha to n to <vocalsound> have y guys , uh , you know , pick <vocalsound> the <disfmarker> y you know , belief - net package
speaker: B
text: No .
speaker: C
text: and tell us what it is , and give us a pointer so we can play with it or something .
speaker: F
text: Sure .
speaker: C
text: And , then as soon as we have it , I think we should start trying to populate it for this problem .
speaker: C
text: Make a first cut at , you know , what 's going on ,
speaker: C
text: and probably the ea easiest way to do that is some on - line way .
speaker: C
text: I mean , you can f figure out whether you wanna make it a web site
speaker: C
text: or <disfmarker> You know , how
speaker: B
text: Uh <disfmarker> I <disfmarker> I <disfmarker> I <disfmarker> um , OK , I <disfmarker> t
speaker: B
text: Yeah .
speaker: B
text: I was actually more joking . With the two or three days .
speaker: C
text: OK , I wasn't .
speaker: B
text: So this was <disfmarker> was a usual jo
speaker: B
text: Um , it will take as long as y y yo you guys need for that .
speaker: C
text: Yeah .
speaker: C
text: Right .
speaker: B
text: But um , maybe it might be interesting if <disfmarker> 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 ,
speaker: B
text: and what it does , and how it does that .
speaker: C
text: Well , y Well , or both of them speak .
speaker: F
text: Sure .
speaker: C
text: We don't care .
speaker: B
text: Yeah , or you can split it up .
speaker: B
text: So , y
speaker: F
text: Hmm .
speaker: B
text: So that will be sort of the assignment for next week , is to <disfmarker> to <disfmarker> for slides and whatever net you picked
speaker: B
text: and what it can do and <disfmarker> and how far you 've gotten .
speaker: B
text: Pppt !
speaker: C
text: Well , I 'd like to also , though , uh , ha have a first cut at what the belief - net looks like .
speaker: C
text: Even if it 's really crude .
speaker: C
text: OK ?
speaker: C
text: So , you know , here a here are <disfmarker>
speaker: E
text: So we 're supposed to @ @ about features and whatnot ,
speaker: E
text: and <disfmarker>
speaker: C
text: Right . Yeah .
speaker: F
text: Mm - hmm .
speaker: C
text: And , as I said , what I 'd like to do is ,
speaker: C
text: I mean , what would be really great is you bring it in <disfmarker> If <disfmarker> if <disfmarker> if we could , uh , in the meeting , say , you know , " Here 's the package ,
speaker: C
text: here 's the current one we have , "
speaker: C
text: uh , you know , " What other ideas do you have ? "
speaker: C
text: and then we can think about this idea of making up the data file .
speaker: C
text: Of ,
speaker: C
text: uh , you know , get a <disfmarker> t a p tentative format for it ,
speaker: C
text: let 's say XML , that says , l you know , " These are the various scenarios we 've experienced . "
speaker: C
text: We can just add to that
speaker: C
text: and there 'll be this <disfmarker> this file of them
speaker: C
text: and when you think you 've got a better belief - net , You just run it against this , um <disfmarker> this data file .
speaker: F
text: So we 'll be like , hand , uh , doing all the probabilities .
speaker: F
text: OK .
speaker: C
text: Oh , yeah , unt until we know more .
speaker: E
text: And what 's the relation to this with <disfmarker> Changing the table so that the system works in English ?
speaker: B
text: OK .
speaker: B
text: So this is <disfmarker> Whi - while you were doing this , I received two lovely emails .
speaker: B
text: The <disfmarker> the full NT and the full Linux version are there .
speaker: B
text: I 've downloaded them both ,
speaker: B
text: and I started to unpack the Linux one <disfmarker>
speaker: B
text: Uh , the NT one worked fine .
speaker: B
text: and I started unta pack the Linux one ,
speaker: B
text: it told me that I can't really unpack it
speaker: B
text: because it contains a future date .
speaker: B
text: So this is the time difference between Germany .
speaker: B
text: I had to wait until one o ' clock this afternoon before I was able to unpack it .
speaker: B
text: Now , um <disfmarker> Then it will be my job to get this whole thing running both on Swede and on this machine .
speaker: B
text: And so that we have it .
speaker: B
text: And then um <disfmarker> Hopefully that <disfmarker> hoping that my urgent message will now come through to Ralph and Tilman that it will send some more documentation along ,
speaker: B
text: we <disfmarker> I control p
speaker: B
text: Maybe that 's what I will do next Monday
speaker: B
text: is show the state and show the system and show that .
speaker: C
text: Yeah .
speaker: C
text: Yeah . So the answer , Johno , is that these are , at the moment , separate .
speaker: C
text: 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 .
speaker: E
text: I guess my question was more about time frame .
speaker: E
text: So we 're gonna do belief - nets this week ,
speaker: E
text: and then <disfmarker>
speaker: C
text: Oh , yeah . I don't know .
speaker: C
text: n None of this is i n Neither of these projects has got a real tight time - line ,
speaker: C
text: in the sense that over the next month there 's a <disfmarker> there 's a deliverable .
speaker: E
text: OK .
speaker: C
text: OK .
speaker: C
text: S so uh , it 's opportu in that sense it 's opportunistic .
speaker: C
text: If <disfmarker> if <disfmarker> 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 <disfmarker>
speaker: C
text: You know , just pppt !
speaker: C
text: go on and do other things .
speaker: E
text: OK .
speaker: B
text: Yeah , but uh <disfmarker> but the uh <disfmarker> This point is really <disfmarker> I think very , very valid that ultimately we hope that <disfmarker> that both will merge into a harmonious and , um , wonderful , um , state
speaker: B
text: 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 ,
speaker: B
text: and now we add this little thing to it " ,
speaker: B
text: you know ?
speaker: B
text: whatever ,
speaker: B
text: Johno 's and Bhaskara 's great belief - net ,
speaker: B
text: and we plug it in ,
speaker: B
text: and then for these certain tasks ,
speaker: B
text: and we know that navigational tasks are gonna be a core domain of the new system ,
speaker: B
text: it all <disfmarker> all of a sudden it does much better .
speaker: B
text: Nuh ?
speaker: B
text: Because it can produce better answers ,
speaker: B
text: tell the person , as I s showed you on this map ,
speaker: B
text: n you know , produce either you know , a red line that goes to the Vista point
speaker: B
text: or a red line that goes to the Tango point
speaker: B
text: or red line that goes to the door ,
speaker: B
text: which would be great .
speaker: B
text: 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 .
speaker: B
text: Rather than taking him always to the geometric center of a building ,
speaker: F
text: Mmm .
speaker: B
text: which is what they do now .
speaker: B
text: And we even had to take out a bit .
speaker: B
text: Nancy , you missed that part .
speaker: B
text: We had to take out a bit of the road work .
speaker: B
text: So that it doesn't take you to the wall <vocalsound> every time .
speaker: D
text: Oh , really ?
speaker: B
text: So . Um <disfmarker>
speaker: B
text: So this was actually an actual problem that we encountered ,
speaker: B
text: which nobody have <disfmarker> has <disfmarker>
speaker: B
text: because car navigation systems don't really care .
speaker: B
text: You know , they get you to the beginning of the street ,
speaker: B
text: some now do the house number .
speaker: D
text: Hmm .
speaker: D
text: Mm - hmm .
speaker: B
text: But even that is problematic .
speaker: B
text: If you go d If you wanna drive to the SAP in Waldorf ,
speaker: B
text: I 'm sure the same is true of Microsoft ,
speaker: B
text: it takes you to the <disfmarker> the address ,
speaker: B
text: whatever , street number blah - blah - blah ,
speaker: B
text: you are miles away from the entrance .
speaker: C
text: Yep .
speaker: B
text: Because the s postal address is maybe a mailbox somewhere .
speaker: D
text: Mm - hmm .
speaker: B
text: Nuh ?
speaker: B
text: but the entrance where you actually wanna go is somewhere completely different .
speaker: B
text: So unless you 're a mail person you really don't wanna go there .
speaker: D
text: Right , yeah .
speaker: C
text: Probably not then ,
speaker: C
text: cuz y you probably can't drop the mail there anyway .
speaker: B
text: Probably neither <disfmarker> e not even that .
speaker: C
text: Yeah .
speaker: C
text: Clear ?
speaker: F
text: OK . Sounds good .
speaker: E
text: The Powder - Tower is made of red limestone .
speaker: D
text: I was wondering .
speaker: D
text: OK .
speaker: B
text: Do you wanna see a picture ?
speaker: D
text: Sure !
speaker: E
text: Sure !
speaker: B
text: Have to reboot for that though .
speaker: D
text: Um .
speaker: D
text: So , you two , who 'll be working on this , li are <disfmarker> are you gl will you be doing <disfmarker> Well , I mean are you supposed to just do it by thinking about the situation ?
speaker: D
text: Can you use the sample data ?
speaker: D
text: Is it like <disfmarker>
speaker: C
text: Of course they use the sample data .
speaker: D
text: Yeah , I mean , ho is there more than <disfmarker>
speaker: D
text: Is there a lot s of sample data that is beyond what you <disfmarker> what you have there ?
speaker: B
text: There <disfmarker> there 's more than I showed ,
speaker: B
text: but um , um , I think this is sort of um , in part my job to look at that
speaker: B
text: and <disfmarker> and to see whether there are features in there that can be extracted ,
speaker: D
text: Yeah .
speaker: B
text: and to come up with some features that are not you know , empirically based on <disfmarker> on a real experiment or on <disfmarker> on <disfmarker> on reality
speaker: D
text: Right .
speaker: D
text: Mm - hmm .
speaker: B
text: but sort of on your intuition of you know , " Aha !
speaker: F
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: B
text: This is maybe a sign for that ,
speaker: B
text: and this is maybe a sign for this . "
speaker: D
text: Mm - hmm .
speaker: E
text: Mm - hmm .
speaker: F
text: So , yeah . Later this week we should sort of get together , and sort of start thinking about that , hopefully .
speaker: B
text: Talk features .
speaker: B
text: Yep .
speaker: C
text: OK . We can end the meeting
speaker: C
text: and call Adam ,
speaker: C
text: and then we wanna s look at some filthy pictures of Heidelberg .
speaker: C
text: We can do that as well .
speaker: C
text: Uh , is that OK ?
speaker: B
text: Well they had <disfmarker> they used the ammunition <disfmarker> They stored the ammunition in that tower .
speaker: C
text: Alright .
speaker: B
text: And that 's why , when it was hit by uh , a cannon ball , it exploded .
speaker: E
text: It exploded .
speaker: C
text: Oh .
speaker: C
text: Ni
speaker: E
text: That 's why they call it the Powder - Tower .
speaker: B
text: Ahh .
speaker: E
text: OK .
speaker: E
text: I first thought it had something to do with the material
speaker: E
text: that it <disfmarker> w that 's why I asked .
speaker: D
text: That 's right , OK .
speaker: B
text: Mmm .

Meeting summary:
text: The initial task of the EDU group is to work on inferring intentions through context.
text: In the navigational paradigm used for the task, these intentions are to "see" to "enter" or to "get to the closest point of" a building.
text: There will be purpose-designed experiments carried out.
text: However, the starting point is, through the use of existing data, to determine possible linguistic, discourse or situation features that define intentionality.
text: These may include the type of building, time of day, particular phrases used or whether the user is a tourist or a native.
text: Initially, these features will be hand-coded, but the goal is to find ways of extracting them automatically from the XML data.
text: Consequently, they will be fed into a belief-net -implemented on a software package like JavaBayes- and the conditional probability of each intention calculated.
text: A prototype system will be put together to test hypotheses regarding both the exact nature of the features and how intentions are derived from them.
text: Inferring intentions in a navigational context is an appropriate task both in project and real-world terms.
text: Its goals are clearly defined and contributre to a smarter system.
text: Although there are some preliminary data to work on, task-specific experiments and recordings will take place.
text: The XML format is going to be used has not been defined, although the SmartKom data can be used as a foundation.
text: In the first instance, the group will have to decide on the software package to be used for the creation and manipulation of the belief-nets.
text: Stability and ease-of-use -in addition to ability to handle XML- of the package are the focus at this stage, instead of the ability to handle large amounts of data.
text: Experts in Bayes nets within ICSI can be consulted on the matter.
text: The decision will be presented in the next meeting along with a first schema of the belief-nets themselves.
text: Possible intermediate nodes can be added to the nets after this.
text: The hypothesis that a set of features from which intentions can be derived exists has to be assessed, before moving on to how to extract these features from the data automatically.
text: Curent navigation systems do not provide for the user's particular intentions when asking for directions.
text: They always compute the shortest path between source and destination.
text: The SmartKom parser, for example, does not mark up data with features adequate for the inference of these intentions.
text: Although it is understandable that language, discourse and situation features will play a role in how they are weighed, the exact nature of those features is unclear.
text: Also hard to evaluate at this stage, is how the assumed features will combine in a belief-net, in order to provide the conditional probabilities of the users' intentions.
text: Even if these problems are solved, extracting the features from the data may prove to be a bottleneck.
text: The existing data are appropriate only for preliminary work, as they don't include intention-related information.
text: On the other hand, the details of the experiments that will have to be designed to get more appropriate data are not clearcut and are yet to be settled.
text: When asking for directions, a user of a navigational device may wish to either view, enter or simply approach a building.
text: This was identified as an initial problem to be tackled through "deep understanding"-type inferences.
text: There is a set of data to start work on from previous work.
text: Similarly, the SmartKom data-format and an ontology developed for the tourist domain (both in XML-standard formats) can be used as groundwork for defining the features, which indicate the user's intentions.
text: For the creation and management of the belief-nets necessary for the task, there are readily available packages -such as JavaBayes- and tools that can provide the infrastructure for a prototype system.