1. lti Intelligent Help (or lack thereof) in Spoken Dialog Systems Dialogs on Dialogs discussion Stefanie Tomko 20-Feb-04 HELP!

2. lti 20-Feb-04Dialogs on Dialogs discussion2 Papers Adding intelligent help to mixed-initiative spoken dialogue systems. G. Gorrell, I. Lewin & M. Rayner. In Proc. of ICSLP, 2002. Targeted help for spoken dialogue systems: intelligent feedback improves naive users' performance. B.A. Hockey, O. Lemon, E. Campana, L. Hiatt, G. Aist, J. Hieronymous, A. Gruenstein, J. Downding. In Proc. of EACL, 2003. ?????  there isn't a lot out there about this!

3. lti 20-Feb-04Dialogs on Dialogs discussion3 We need Help! 56% of NL system users in experiment asked for help without explicit knowledge that they could do so Speech Graffiti users knew about various help/orientation keywords –91% used options –70% used where was I? –48% used help

4. lti 20-Feb-04Dialogs on Dialogs discussion4 What is Help? How do I do ? What can I do? Where was I? How do I say that? I didn't understand what you said….

5. lti 20-Feb-04Dialogs on Dialogs discussion5 User-initiated Help examples NL Movieline: –Wordy, general This system allows you to obtain movie and theater information for Pittsburgh. You can ask for the location, phone number, and movie listing for a certain theater. Or, you can ask about a particular movie to get the rating and or genre find out where it is playing. Specify both a movie and theater to obtain showtimes. If you get stuck, you can say Reset, to start over. Jupiter –Example based You can ask about general weather forecasts as well as information on temperature, windspeed,… Try saying one of the following: 'what's the weather for Denver?' 'what cities do you know about?' 'what do you know about besides weather?' 'what can I say?' Try saying one of the following 'are there any advisories for the United States?' 'what is the extended forecast for Boston?' 'will it rain in Toronto?'…

6. lti 20-Feb-04Dialogs on Dialogs discussion6 User-initiated Help examples Speech Graffiti –Somewhat "state" based slot + options : you can say, rating is... G, PG, PG-13, R, NC-17, not rated, or you can ask, what is the rating? options : you can specify or ask about title, show time, day… help : gives list of keywords on 1 st round, then gives explanation of keyword functions TellMe –Orientation, or, at main level, lots of general system info You're in Sports, in the NHL section.

7. lti 20-Feb-04Dialogs on Dialogs discussion7 These are all kind of "dumb" They might not take system state into account They aren't smart about what users really want to do They might not tell users exactly how to speak They might not orient users to where they are in the system But at least they give users some information…

8. lti 20-Feb-04Dialogs on Dialogs discussion8 System-initiated "Help" examples NL Movieline: –Excuse me? –Didn't catch that. Jupiter –Pardon me? Speech Graffiti –I'm sorry, I'm having trouble understanding you TellMe –I'm sorry, I didn't get that. Please say a category in Travel. These are really dumb!

9. lti 20-Feb-04Dialogs on Dialogs discussion9 Intelligent/Targeted Help Makes system-initiated help a little smarter Goal: provide immediate feedback, tailored to what the user said, for cases in which the system was not able to understand an utterance Kind of different perspective compared to traditional error handling What should I do to deal with this error? How can I help the user not make this error in the future?

10. lti 20-Feb-04Dialogs on Dialogs discussion10 Gorrell et al ICSLP paper Grammar-based vs. statistical LMs –Grammars easy to create (?) –GB performs better if users know what to say –SLMs better for unusual & less constrained utts 1 st attempt – recognition only (i.e. no help) –Run all utts through GBLM &SLM, choose based on confidence scores –Not reliable enough

11. lti 20-Feb-04Dialogs on Dialogs discussion11 On/Off House User initiative Natural language –Turn off the light in the bathroom –Are the hall and kitchen lights switched on? –Could you tell me which lights are on?

12. lti 20-Feb-04Dialogs on Dialogs discussion12 Targeted Help Grammar- based LM parsable? Play regular output Send to SLM Classify result Play appropriate help message yes no

13. lti 20-Feb-04Dialogs on Dialogs discussion13 Classification Hand-classified training set 12 classes 24 features Most common classes –REFEXP_COMMAND (35%) I didn't quite catch that. To turn a device on or off, you could try something like 'turn on the kitchen light.' –LONG_COMMAND (13%) I didn't quite catch that. Long commands can be difficult to understand. Perhaps try giving separate commands for each device. –PRON_COMMAND (11%) I didn't quite catch that. To change the status of a device or group of devices you've just referred to, you could try for example 'turn it on' or 'turn them off.'

14. lti 20-Feb-04Dialogs on Dialogs discussion14 Evaluation Baseline classification error: 65% Cross-validated final decision tree error: 12% Between-subjects user study task –call a voice-enabled house & leave it in a secure state No training Targeted help (N=16) vs. control help (N=15)

15. lti 20-Feb-04Dialogs on Dialogs discussion15 Results Targeted helpControl help WER (GB only?) 39%55% Grammaticality 47%36% WER(?): 1 st 5 utts 45%76%

16. lti 20-Feb-04Dialogs on Dialogs discussion16 Results (2) Targeted help group had more variety in constructions Targeted help users requested help more often –Six TH users vs. only one (!) control user Longer dialogs in TH groups –Some of this is system exploration No significant differences in awareness of final house state or perception of systems' abilities No comparison of task completion

17. lti 20-Feb-04Dialogs on Dialogs discussion17 Hockey et al EACL paper Domain: WITAS command & control for robotic helicopter Targeted Help is an independent module Grammar- based LM parsable? Play regular output Send to SLM Create & play appropriate help message yes no SLM parsable ? yes no

18. lti 20-Feb-04Dialogs on Dialogs discussion18 Help message content Message contains one or more of –A. What the system heard A report of the backup SLM recognition hypothesis –B. What the problem was (diagnostic) A description of the problem with the user's utterance –C. What you might say instead A similar in-grammar example Rule-based determination of exact content for B & C Not clear how often A B & C appear & in what combinations

19. lti 20-Feb-04Dialogs on Dialogs discussion19 B. Diagnostic Endpointing –Check if initial recognized word is ok initial parsable-input word Out-of-volcabulary –Compare SLM vocab to GBLM vocab Subcategorization –Check features of verbs in SLM hypothesis Zoom in [+intrans] => ! Zoom in on the red car

20. lti 20-Feb-04Dialogs on Dialogs discussion20 C. In-grammar example Try to use words & dialog-move type from user's original utterance –wh-question –yn-question –answer –command Fly over to the hospital GBLM: [reject] SLM:fly hospital TH: fly to the hospital (how does TH know this is a command?)

21. lti 20-Feb-04Dialogs on Dialogs discussion21 Evaluation Between-groups user study –Targeted help vs. no help –Was user-initiated help available? N=20, 5 tasks each –Only T1 & T5 assessed –Locate an x and then land at the y

22. lti 20-Feb-04Dialogs on Dialogs discussion22 Results Significantly fewer TH users gave up on tasks –Control users gave up on 39% of tasks –TH users gave up on only 6% Time to completion effects –Hard to measure "completion!" –Task (=> users get better over time) –Help x Task –Help alone (p<.1 in "lenient" analysis)

23. lti 20-Feb-04Dialogs on Dialogs discussion23 Discussion Definitely an improvement over "dumb" options How easy are these options to automate and port to new domains/systems? –Classifier version needs training data –Rule-based version needs… rules Is there such a thing as too smart? The system doesn't understand the word X The system doesn't understand the word X used with the red car

24. lti 20-Feb-04Dialogs on Dialogs discussion24 Discussion (2) Do grammaticality improvements fostered by TH persist? How frequently is TH activated? –Does frequency decrease over time? At a faster rate cf. plain-old help? –In rule-based system, how often do both LMs fail?

25. lti 20-Feb-04Dialogs on Dialogs discussion25 Discussion (3) How often does either system (esp. rule-based) provide inappropriate help? –Wrong dialogue-move type? –Wrong vocabulary? What % of 1 st -utt-after-TH are grammatical? –cf. plain-old help Are there other ways to implement/ supplement TH? –State information? –Back-off to directed dialog? (in worst case…)

26. lti 20-Feb-04Dialogs on Dialogs discussion26 Anything else? Let me know if you come across any more references to this sort of thing…