you want to view things you wouldn't be in a hurry Or they might be more likely to be using the place that they want to go to as a like a navigational point to go to another place Whether the destination was their final destination whether the destination was closed Those are all And then look at the OK So that means that I should switch to the other program Um right now still kind of in a toy version of it because we didn't know the probabilities of or Well talk about it when I get the picture up No one knows it OK So this right what we see What happens if I maximize this There we go But uh So The mode basically has three different outputs The probability whether the probability of a Vista Tango or Enter Um The context we simplified Basically just the businessman the tourist unknown Verb used is actually personally amusing mainly because just whether the verb is a Tango verb an Enter verb or a Vista verb Yeah that one needs And a are lot those mutually exclusive Not at all sets No that Right needs a lot of work Got But uh that made the probably significantly be more complicated to enter Yeah so we decided that for the purposes of this be Simple to just Yeah have three Stab verbs Yep Right Um Why don't you mention things about this Bhaskara that I am not that are not coming to my mind right now OK so Yeah so note the four nodes down there the sort of the things that are not directly extracted Actually the five things The closed is also not directly extracted I guess from the uh Well From i the Hmm Actually utterance wait sort of It is is because OK closed because sort have is the the time of day and the close it just had the Right er and so time it closed Right but the other ones the final destination the whether doing business whether in a hurry and whether tourists that kind of thing is all uh sort of you know probabilistically depends Inferred on from the the other other things Yeah OK And the mode you know depends on all those things only Yeah the the actual parse is somewhere Yeah up So we around haven't here managed Like we don't have nodes for discourse and parse although like in some sense they are parts of this But uh The idea is that we just extract those features from them so we don't actually have a node for the entire parse Right because never do inference on it anyway so So some of the the top row of things Disc admission whether fee discuss the admission fees So we looked at the data Oh data people were saying things like Can I get to this place What is the admission fee So like a huge uh clue that trying to Enter the place rather than uh to Right Tango OK so see There were be other things besides just the admission fee but you know we didn't That have like our example That was the initial one OK found So there are certain cues that are very strong the discourse either that lexical Yeah um concept cues for one of those And then in that second row or whatever that row of Time of Day through that So all of those Some of them come from the utterance and some of them are sort of either world knowledge or situational things Right So that you Right distinction between those and One uh Uh Um anything else you want to say Bhaskara Um Unmark Yeah I m I mean Time of Day One thing uh Yeah are a couple of more things I mean Uh I would actually suggest we go through this one more time so we we all uh agree on what what the meaning of these things is at the moment and maybe what changes Yeah th OK we so one thing I you know unsure about is how we have the discus uh the admission fee thing set up So one thing that we were thinking was by doing the layers like this Uh we kept um things from directly affecting the mode beyond the concept but you could see perhaps discus the admission fee going directly to the mode pointing at Enter right Versus pointing to just at tourist OK But we just decided to keep all the things we extracted to point at the middle and then down Why is the landmark OK The landmark is facing to the tourists because talking about landmarks as touristic landmarks Right not Yeah as possible um Navigational Navigational navigational landmarks so Yeah Then be whatever building they referred to Right So see The variables Disc admission fee is a binary thing time of day is like morning afternoon night Is that the deal Yeah we have it currently set up but Yep could Yeah be you know based Yeah hour or Whatever Yeah granularity discrete it Yeah des it Yeah Normally context will include a huge amount of information but um we are just using the particular part of the context which consists of the switch that they flick to indicate whether a tourist or not Yep guess OK So given in their Right input So Right Right so not really all of context Similarly prosody is not all of prosody but simply for our purposes whether or not they appear tense or relaxed very nice huh OK and the So the context is a switch between tourist or Or also Or unknown un unknown Yeah yeah OK Unknown So right final dest So it seems like that would really help you for doing business versus tourist Which is th Which but OK so the the context being um e I don't know if that sort of in general are you I mean the ar ar are do they allow business people to be doing things at the moment Yeah it does OK So Everything you is just probablistic probabilities over OK over always which of those it is Yeah Um right So then landmark is Oh sorry Verb used is like right now we only have three values but in general they would be a probability distribution over all verbs Rather let me rephrase that It it can take values in the set of all verbs that they could possibly use Um nice walls is binary closed is binary final destination again Yeah all those are binary I guess And mode is one of three things So the the middle layer is also binary No Yeah anything with a question mark after it in that picture is a binary node Uh It Yeah But all those things without question marks are also binary Right Which things Nice Wi walls Oh Nice walls is uh something that we extract from our world knowledge Yeah a Oh yeah Sorry It is binary It is binary but true doesn't have question mark because OK extracted point Yeah