PhD A: Right .
Grad C: OK . Then I suggest we move on to the to we have um uh the EDU Project , let me make one more general remark , has sort of two two side uh um actions , its um action items that we 're do dealing with , one is modifying the SmartKom parser and the other one is modifying the SmartKom natural language generation module . And um this is not too complicated but I 'm just mentioning it put it in the framework because this is something we will talk about now . Um , I have some news from the generation , do you have news from the parser ?
Grad F: Um , not Grad C: By that look I Grad F: Yes , uh , I would really p It would be better if I talked about it on Friday .
Grad C: OK .
Grad F: If that 's OK .
Grad C: Yeah , wonderful . Um , did you run into problems or did you run into not h having time ?
Grad F: Yeah . But not not any time part .
Grad C: OK , so that 's good . That 's better than running into problems .
Grad F: OK .
Grad C: And um I I do have some good news for the natural language generation however . And the good news is I guess it 's done . Uh , meaning that Tilman Becker , who does the German one , actually took out some time and already did it in English for us . And so the version he 's sending us is already producing the English that 's needed to get by in version one point one .
Grad F: So I take it that was similar to the what what we did for the parsing ?
Grad C: Yeah . I I it even though the generator is a little bit more complex and it would have been , not changing one hundred words but maybe four hundred words ,
Grad F: OK .
Grad C: but it would have been
Grad F: OK .
Grad C: but this this is I guess good news , and the uh the time and especially Bhaskara and uh and um Oh do I have it here ? No . The time is now pretty much fixed . It 's the last week of April until the fourth of May so it 's twenty - sixth through fourth . That they 'll be here . So it 's it 's extremely important that the two of you are also present in this town during that time .
Grad B: Wait , what what are the days ? April twenty - sixth to the May fourth ?
Grad C: Yeah , something like that .
Grad B: I 'll probably be here .
Grad C: It 's Grad F: Yeah .
Grad E: You will be here .
Grad C: There is a d Isn't finals coming up then pretty much after that ?
Grad F: Finals was that .
Grad G: Yeah w it doesn't really have much meaning to grad students but final projects might .
Grad C: OK .
Grad F: Yeah actually , that 's true .
Grad G: That Grad C: Anyway , so this is Grad B: Well I 'll be here working on something . Guaranteed , it 's just uh will I be here , you know , in uh I 'll be here too actually but PhD A: Hmm .
Grad C: No it 's just um you know they 're coming for us so that we can bug them
Grad G: Ye
Grad C: and ask them more questions and sit down together and write sensible code and they can give some nice talks and stuff . But uh
Grad B: But it 's not like we need to be with them twenty - four hours a day s for the seven days that they 're here .
Grad C: just make a Not not unless you really really want to .
Grad E: They 're very dependent
Grad C: Not unless you really want to . And they 're both nice guys so you may may want to . OK , that much from the parser and generator side , unless there are more questions on that .
Grad G: So , no sample generator output yet ?
Grad C: No . It Just a mail that , you know , he 's sending me the the the stuff soon
Grad G: OK . This is being sent , mm - hmm . OK .
Grad C: and I was completely flabbergasted here
Grad G: Mm - hmm .
Grad C: and I and that 's also it 's it 's going to produce the concept - to - speech uh blah - blah - blah information for necessary for one point one in English based on the English , you know , in English . So . I was like " OK ,
Grad E: We 're done .
Grad C: we 're done ! "
Grad G: So that was like one of the first l You know , the first task was getting it working for English . So that 's basically over now . Is that right ?
Grad C: Yeah .
Grad G: So the basic requirement fulfilled .
Grad C: Um , the basic requirement is fulfilled almost . When Andreas Stolcke and and his gang ,
Grad G: Mm - hmm .
Grad C: when they have um changed the language model of the recognizer and the dictionary , then we can actually a put it all together
Grad G: Mm - hmm . So the speech recognizer also works . Uh - huh . Mm - hmm .
Grad C: and you can speak into it and ask for TV and movie information
Grad E: Toll .
Grad C: and then when if if something actually happens and some answers come out , then we 're done .
Grad G: Mm - hmm . If and they 're kind of correct .
Grad E: So it 's not done basically .
Grad C: Hmm ?
Grad G: And they kind of are are correct .
Grad E: Right . Perhaps if the answers have something to do with the questions for example .
Grad G: It 's not just like anything . And they 're mostly in English . So .
Grad C: Then um Grad G: Are they is it using the database ? the German TV movie .
Grad C: Yeah .
Grad G: OK . So all the actual data might be German names ?
Grad C: Um well actually th um
Grad G: Or are they all like American TV programs ?
Grad C: um well Grad E: I want to see " Die Dukes Von Hazard "
Grad C: The OK , so you don't know how the German dialogue uh the German the demo dialogue actually works . It works the first thing is what 's , you know , showing on TV , and then the person is presented with what 's running on TV in Germany on that day , on that evening
Grad G: Mm - hmm , mm - hmm .
Grad C: and so you take one look at it and then you say " well that 's really nothing there 's nothing for me there " " what 's running in the cinemas ? " So maybe there 's something better happening there .
Grad G: Mm - hmm .
Grad C: And then you get you 're shown what movies play which films , and it 's gonna be of course all the Heidelberg movies and what films they are actually showing .
Grad G: Mm - hmm .
Grad C: And most of them are going to be Hollywood movies . So , " American Beauty " is " American Beauty " ,
Grad G: Hmm .
Grad C: right ? Yeah .
Grad E: Mm - hmm .
Grad G: Right .
Grad C: And um .
Grad G: But they 're shown like on a screen .
Grad C: N
Grad G: It 's a I mean so would the generator , like the English language sentence of it is " these are the follow you know the following films are being shown " or something like that ?
Grad C: Yeah , but it in that sense it doesn't make In that case uh it doesn't really make sense to read them out loud .
Grad G: S Right .
Grad C: if you 're displaying them .
Grad G: So it 'll just display OK .
Grad C: But uh it 'll tell you that this is what 's showing in Heidelberg and there you go .
Grad G: So we don't have to worry about um Yeah .
Grad C: And the presentation agent will go " Hhh ! " {comment} Nuh ?
Grad G: OK .
Grad C: Like that the avatar .
Grad G: OK .
Grad C: And um . And then you pick pick a movie and and and it show shows you the times and you pick a time and you pick seats and all of this . So .
Grad G: OK .
Grad C: Pretty straightforward .
Grad E: OK .
Grad C: But it 's so this time we we are at an advantage because it was a problem for the German system to incorporate all these English movie titles .
Grad G: Yeah .
Grad C: Nuh ? But in English , that 's not really a problem ,
Grad G: Right . Mm - hmm .
Grad C: unless we get some some topical German movies that have just come out and that are in their database . So the person may select " Huehner Rennen " or whatever .
Grad G: Right .
Grad E: " Chicken Run " .
Grad C: OK . Then uh on to the modeling . Right ?
Grad B: Yeah , yeah , I guess .
Grad C: Um then modeling , there it is .
Grad B: Yep .
Grad E: OK . What 's the next thing ?
Grad B: e
Grad C: This is very rough but this is sort of what um Johno and I managed to come up with . The idea here is that Grad B: This is the uh s the schema of the XML here , not an example or something like that .
Grad C: Yeah this is not an XML this is sort of towards an a schema ,
Grad E: OK .
PhD A: Right .
Grad C: nuh ? definition . The idea is , so , imagine we have a library of schema such as the Source - Path - Goal and then we have forced uh motion , we have cost action ,
Grad E: Mm - hmm .
Grad C: we have a whole library of schemas .
Grad G: Mm - hmm .
Grad C: And they 're gonna be , you know , fleshed out in in their real ugly detail , Source - Path - Goal , and there 's gonna be s a lot of stuff on the Goal and blah - blah - blah , that a goal can be and so forth . What we think is And all the names could should be taken " cum grano salis " . So . This is a the fact that we 're calling this " action schema " right now should not entail that we are going to continue calling this " action schema " . But what that means is we have here first of all on the in the in the first iteration a stupid list of Source - Path - Goal actions
Grad B: Actions that can be categorized with or that are related to Source - Path - Goal .
Grad C: wi to that schema
Grad E: OK .
Grad G: Mm - hmm .
Grad C: and we will have you know forced motion and cost action actions .
Grad B: And then those actions can be in multiple categories at the same time if necessary .
Grad C: So a push may be in in in both you know push uh in this or this uh Grad G: Forced motion and caused action for instance ,
Grad C: Exactly . Yeah .
Grad G: OK .
Grad C: Also , these things may or may not get their own structure in the future . So this is something that , you know , may also be a res As a result of your work in the future , we may find out that , you know , there 're really s these subtle differences between um even within the domain of entering in the light of a Source - Path - Goal schema , that we need to put in fill in additional structure up there . But it gives us a nice handle . So with this we can basically um you know s slaughter the cow any anyway we want . Uh . It it is It was sort of a it gave us some headache , how do we avoid writing down that we have sort of the Enter Source - Path - Goal that this But this sort of gets the job done in that respect and maybe it is even conceptually somewhat adequate in a sense that um we 're talking about two different things . We 're talking more on the sort of intention level , up there , and more on the this is the your basic bone um schema , down there .