B: And this do they don't wanna sh see it on a map ,
B: or they don't wanna know it 's five hundred yards away from you , or that it 's to the your north .
B: They wanna go there .
B: That 's what they say , is , " Where is it ? " .
B: Where is that damn thing ?
E: And the parser would output B: Well , that 's a a question mark .
B: sh
B: A lot of parsers , um ,
B: just , uh B: That 's way beyond their scope , is of interpreting that .
B: You know ?
B: But um ,
B: still outcome w the outcome will be some form of structure ,
B: with the town hall and maybe saying it 's a WH focus on the town hall .
B: But to interpret it ,
D: Mm - hmm .
B: you know ?
B: somebody else has to do that job later .
E: I 'm just trying to figure out what the SmartKom system would output ,
C: Yeah .
E: depending on these things .
B: Um , it will probably tell you how far away it is ,
B: at least that 's That 's even what Deep Map does .
B: It tells you how far away it is ,
B: and and shows it to you on a map .
B: Because i we can not differentiate , at the moment , between , you know , the intention of wanting to go there or the intention of just know wanting to know where where it is .
D: People no might not be able to infer that either , right ?
D: Like the fact Like , I could imagine if someone came up to me and asked , " Where 's the city hall ? " , I might say , g ar " Are you trying to get there ? "
D: Because how I describe um , t its location 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 .
B: Mm - hmm .
B: It 's a granularity factor ,
C: Yeah .
B: because where people ask you , " Where is New York ? " , you will tell them it 's on the East Coast .
D: Uh - huh .
D: Yeah . Exactly .
D: Right .
B: Y y eh you won't tell them how to get there , ft you know , take that bus to the airport and blah - blah - blah .
D: Right .
D: Yeah .
D: Right .
B: But if it 's the post office , you will tell them how to get there .
D: Mm - hmm .
B: So th They have done some interesting experiments on that in Hamburg as well .
D: Right .
D: Right .
B: So .
C: But C: i Go go back to the the uh , th
C: Yeah , that slide .
B: So I w this is " onto " is is knowledge about buildings ,
B: their opening times ,
B: and then t coupled with time of day , um , this should B: You know .
D: So that context was like , um , their presumed purpose context ,
D: i like business or travel ,
D: as well as the utterance context , like , " I 'm now standing at this place at this time " .
C: Yeah , well I think we ought to d a As we have all along , d We we 've been distu distinguishing between situational context ,
C: which is what you have as context ,
C: and discourse context ,
B: Mm - hmm .
C: which you have as DH ,
C: I don't know what the H means .
B: Nuh .
B: History .
B: Discourse history .
C: OK .
C: Whatever .
B: Yeah .
C: So we can work out terminology later .
B: Yep .
C: So , they 're they 're quite distinct .
C: I mean , you need them both ,
C: but they 're quite distinct .
C: And , so what we were talking about doing , a a as a first shot , is not doing any of the linguistics .
C: Except to find out what seems to be useful .
C: So , the the the reason the belief - net is in blue , is the notion would be C: Uh , this may be a bad dis bad idea ,
C: 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 ,
C: given
C: these We have all these transcripts
C: and we 're able to , by hand , extract the features to put in the belief - net .
C: Saying , " Aha !
C: here 're the things which , if you get them out of out of the language and discourse , and put them into the belief - net , it would tell you which of these three uh , intentions is most likely . "
C: And if to actually do that , build it , um you know , run it y y run it on the data where you hand - transcribe the parameters .
C: And see how that goes .
C: If that goes well , then we can start worrying about how we would extract them .
C: So where would you get this information ?
C: And , expand it to to other things like this .
C: But if we can't do that , then we 're in trouble .
C: I mean th th i i if you can't do this task ,
C: um B: We need a different , uh , engine .
C: Uh , uh , yeah , or something .
B: Machine , I mean .
C: Well it i I if it if it 's the belief - nets , we we 'll switch to you know , logic or some terrible thing ,
C: but I don't think that 's gonna be the case .
C: 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 .
C: The real issue is , do what are the factors involved in determining this ?
C: And I don't know .
B: Hmm .
B: But , only w
C: Hold on a s Hold on a second .
B: Muh .
C: So , I know .
C: Uh , uh , is it clear what 's going on here ?
F: Yep .
D: Um , I missed the beginning ,
D: but , um
D: I guess D: could you back to the slide ,
D: the previous one ?
D: So , is it that it 's , um D: These are all factors that uh , a These are the ones that you said that we are going to ignore now ?
D: or that we want to take into account ?
D: You were saying n
C: Take them into account .
D: Take the the linguistic factors too .
C: But but you don't worry about h
D: Oh , how to extract these features .
C: how to extract them .
D: OK .
C: So , f let 's find out which ones we need first ,
D: Got it .
D: OK .
D: And and it 's clear from the data , um , like , sorta the correct answer in each case .
C: and C: No .
D: But l
D: OK .
B: No .
D: That 's that 's the thing I 'm curious ab
B: But C: Let 's go back to th Let 's go back to the the the slide of data .
D: Like do we know from the data wh which B: Um D: OK .
D: So B: Not from that data .
B: But , um , since we are designing a a a an , compared to this , even bigger data collection effort , <comment> um , we will definitely take care to put it in there ,
D: Mm - hmm .
D: Mm - hmm .
D: Mm - hmm .
B: in some shape , way , form over the other ,
C: Yeah .
B: to see whether we can , then , get sort of empirically validated data .
D: Right .
B: Um , from this , we can sometimes , you know B: an and that 's that but that isn't that what we need for a belief - net anyhow ?
B: is sort of B: s sometimes when people want to just see it , they phrase it more like this ?
D: Mm - hmm .
B: But it doesn't exclude anybody from phrasing it totally differently , even if they still D: Right .
D: Right .
B: you know ?
B: But then other factors may come into play that change the outcome of their belief - net .
D: Right .
B: So , um , this is exactly what B: Because y you can never be sure .
B: 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 . "
D: Sure .
B: You know , because then , uh , you D: u u I mean , the only way you could get that is if you were to give th the x subjects a task .
D: Right ?
D: Where you have where your , uh , current goal is to B: We
B: Yeah !
B: That 's what we 're doing .
D: So that 's what you want ?
B: But but we will still get the phrasing all over the place .
D: OK .
D: So you will know .
D: Mm - hmm .
B: I 'm sure that , you know C: Yeah .
D: The No , that 's fine .
D: I guess , it 's just knowing the intention from the experimental subject .
C: Yeah .
B: Mm - hmm .
C: From that task , yeah .
C: So , uh , I think you all know this , but we are going to actually use this little room
C: and start recording subjects probably within a month or something .
C: So , this is not any lo any of you guys ' worry ,
C: except that we may want to push that effort to get information we need .
C: So our job is to figure out how to solve these problems .
C: 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 .
C: 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 .
C: So , yeah y you 're absolutely right , though .
C: No , you you will not have ,
C: and there it is ,
C: and , uh C: But you know , y y
C: the , um D: And I think the other concern that has come up before , too , is if it 's D: um D: I don't know if this was collected what situation this data was collected in .
D: Was it is it the one that you showed in your talk ?
D: Like people B: No , no .
D: But
B: No .
D: OK .
D: So was this , like , someone actually mobile , like s using a device ?
B: Uh ,
B: N no , no not B: i it was mobile
B: but not not with a w a real wizard system .
B: So there were never answers .
D: Uh - huh .
D: OK . OK .
D: But , is it D: I guess I don't know The situation of of collecting th the data
D: of , like Here you could imagine them being walking around the city . as like one situation .
D: 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 .
B: Mm - hmm .
D: If they 're doing it in a D: you know , " I 'm sitting here with a map and asking questions " ,