1. Belief Updating in Spoken Dialog Systems Dialogs on Dialogs Reading Group June, 2005 Dan Bohus Carnegie Mellon University, January 2004

2. 2 Misunderstandings  Misunderstandings are an important problem in spoken dialog systems  System obtains an incorrect semantic interpretation of the users’ utterance  15-40% of turns  Significant negative impact on overall success rate

3. 3 Confidence annotation  Use confidence scores to guard against potential misunderstandings  Traditionally: from speech recognition engine [Chase, Bansal, Cox, Kemp, etc]  Focuses on WER, not tuned to task at hand  More recently: system-specific semantic confidence scores [Carpenter, Walker, San-Segundo, etc]  Integrate knowledge from different levels in the system: speech recognition, language understanding, dialog management

4. 4 Correction Detection  Detect whether or not the user is trying to correct the system  Related: aware-site detection  Similar ML approaches using multiple sources of knowledge [Litman, Swerts, Krahmer, etc]

5. 5 S: Where are you flying from? U: [CityName={Aspen/0.6; Austin/0.2}] S: Did you say you wanted to fly out of Aspen? U: [No/0.6] [CityName={Boston/0.8}] Proposed: Belief Updating  Integrate confidence annotation and correction detection in a unified framework for continuously tracking beliefs [CityName={Aspen/?; Austin/?; Boston/?}]  A “belief updating” problem: initial belief + system action + user response updated belief

6. 6 Formally…  Given:  An initial belief P initial (C) over concept C  A system action SA  A user response R  Construct an updated belief P updated (C)  As “accurate” as possible  P updated (C) ← f (P initial (C), SA, R)

7. 7 Examples

8. 8 Examples - continued

9. 9 Outline  Introduction  Data  A simplified version of the problem. Approach  User behaviors  Learning: Preliminary results  More on evaluation  Where to from here? data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

10. 10 Data  Collected in an experiment with RoomLine  Phone-based, mixed initiative system for making conference room reservations  Equipped with explicit and implicit confirmations  Corpus statistics  46 participants  449 sessions, 8278 turns  13.5% misunderstandings [9.8% / 22.5%]  25.6% WER [19.6% / 39.5%]  11362 concept updates data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

11. 11 System actions and concept updates  Explicit and implicit confirmations Start time: Explicit Confirmation/grounding [EC] Date: Implicit Confirmation/grounding [IC] data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

12. 12 System actions and concept updates Date: Implicit Confirmation/grounding [IC] Start time: Implicit Confirmation/grounding [IC] End time: Implicit Confirmation/task [ICT]  Implicit Confirmations Task data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

13. 13 # of Conflicting Hypotheses  Below 3% involve more than 1 hypothesis  System not using multiple hypotheses  [Future work: regenerate multiple hypotheses in batch] data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

14. 14 Outline  Introduction  Data  A simplified version of the problem. Approach  User behaviors  Learning: preliminary results  More on evaluation  Where to from here? data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

15. 15 A Simplified Version Given only 3% have more than 1 hypothesis,  Update belief in the top-hypothesis after implicit and explicit confirmations  Instead of  P updated (C) ← f (P initial (C), SA, R)  Do  ConfTop updated (C) ← f (ConfTop initial (C), SA, R)  For SA = {EC, IC, ICT} data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

16. 16 Approach  Use machine learning  Dataset  Concept updates for EC, IC, ICTs  Features  Initial confidence score ConfTop initial (C)  System action (SA)  User response (R)  Target  Updated confidence score ConfTop updated (C)  Data is labeled, so we have a binary target data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

17. 17 Outline  Introduction  Data  A simplified version of the problem. Approach  User behaviors  Learning: preliminary results  More on evaluation  Where to from here? data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

18. 18 User behaviors  Study of user behaviors in response to ICs and ECs  Can inform feature selection and feature development  Provide insights into where the difficulties are  Can inform potential strategy refinements data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

19. 19 User responses to ECs  Transcripts  Decoded YESNOOther CORRECT 1097 [94.2% of cor] 862 INCORRECT3 202 [69.9% of inc] 84 YESNOOther CORRECT 1016 [87.3% of cor] 11137 INCORRECT2 171 [69.9% of inc] 116 ~10% data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

20. 20 “Other” Responses to EC  “Eyeball” estimates (out of 146 responses)  ~70% simply repeat the correct concept value That should come in as a handy feature  ~10% change conversation focus  ~10% turn overtaking issues Maybe inhibit barge-in until Antoine finishes his thesis  ~10% other data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

21. 21 User responses to ICs  Transcripts  Decoded YESNOOther CORRECT 166 [31.3% of cor] 38326 INCORRECT15 75 [31.5% of inc] 148 YESNOOther CORRECT 151 [28.5% of cor] 20369 INCORRECT16 62 [26.1% of inc] 160 data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

22. 22 Users Don’t Always Correct ICs  Actually, they corrected in 45% of the cases User does not correct User corrects CORRECT5571 INCORRECT 126 [55% of incor] 104 [45% of incor]  That means if we knew exactly when they correct, we’d still have (126+1)/788 = 16% error  So what do users do when they don’t correct?  They may actually correct partially  Completely ignore the error … (if non-essential)  Readjust to accommodate task data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

23. 23 More questions…  Understand better this “ignore” phenomenon  Impact on task success? IC correction rate: 49% (successful tasks) vs 41% (unsuccessful) Fixed vs more “flexible” scenarios  Impact of prompt length on P(user will correct)?  “Essential” vs “non-essential” concepts? data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

24. 24 Outline  Introduction  Data  A simplified version of the problem. Approach  User behaviors  Learning: preliminary results  More on evaluation  Where to from here? data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

25. 25 Which ML technique?  Need good probability outputs  Margins produced by discriminant classifiers are inadequate  If you want probability scores, i.e. conf = 0.85 means that in 85% of cases with conf=0.85 the concept is right evaluate on a soft-metric [I’ll contradict myself later!! ]  Step-wise logistic regression  Sample-efficient  Feature selection  Good soft-metric performance optimizes for avg. log likelihood of data data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

26. 26 Data. Features  For each system action {EC, IC, ICT}  Initial Confidence score  Other indicators about current state: How well has the dialog been going Which concept are we talking about How far back was this concept acquired  Features on user response Confirmation and Disconfirmation markers Acoustic / Prosodic: f0 (min, max, range, maxslope, etc) + normalized versions Num words; turn length (secs) Concept information: expected / repeated / new concepts and grammar slots… Confidence Barge-in & Timeout info Lexical features (preselected by MI with “target” or confirm/disconfirm markers) data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

27. 27 Results  Actually using a 1-level logistic model-tree  Split on answer_type = {yes, no, other, no_parse}  Perform step-wise logistic regression on the 4 leaves P-entry = 0.05 P-reject = 0.30 BIC stopping criterion  Also tried full-blown model tree, results are similar, maybe marginally worse data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

28. 28 Explicit Confirmation HARDSOFT Initial31.1%-0.5076 Heuristic8.6%-0.1943 LMT(CV)3.7%-0.1160 LMT(training)2.9%-0.0851 data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

29. 29 Implicit Confirmation HARDSOFT Initial31.4%-0.6217 Heuristic24.0%-0.6736 LMT(CV)19.6%-0.4521 LMT(training)18.8%-0.4124 Oracle Baseline16.1%- data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

30. 30 Outline  Introduction  Data  A simplified version of the problem. Approach  User behaviors  Learning: preliminary results  More on evaluation  Where to from here? data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

31. 31 What can Logistic Regression / AVG-LL do for you?  D = {d 1, d 2, d 3, d 4, …} d i = 1/0  P(D) = ∏P(d i =1 | x i )  Express density P(d i =1 | x i ) as:  P(d=1 | x ) = 1 / (1 + exp(- wx )) You can actually derive this if you start with P(x | d) gaussian  Find parameters w to max(P(D))  argmax(P(D)) = argmax ∏P(d i =1 | x i )  argmax(P(D)) = argmin ∑-log(P(d i =1 | x i ))  Hence we maximize the average log-likelihood  But what does that mean? data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

32. 32 Loss function in Logistic Regression  Log-likelihood loss function 0.01 0.1 0.7 0.8 1 If d=1, then P(d=1)=0.01 is ten times worse than P(d=1)=0.1, but P(d=1)=0.7 is about the same as P(d=1)=0.8 Things are mirrored for d=0 d=1 This does not match the “threshold” model commonly used to engage actions data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

33. 33 A New Loss Function: T2  A loss function that better matches our domain: T2 (or even T3)  Optimize argmax ∑ T2(P(d i =c | x i ))  Not differentiable   Not convex  0 t1 t2 1 d=1 C1 C2 0 t1 t2 1 d=0 C3 C4 data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

34. 34 Smoothed version  A loss function that better matches our domain: T2 (or even T3)  Optimize argmax ∑ SmoothT2(P(d i =c | x i ))  Differentiable!  But still not convex  … multiple local maxima 0 t1 t2 1 d=1 C1 C2 SmoothT2(p) = σ 1 (p) + σ 2 (p) σ i (p) = 1 / (1+exp(k i (p-θ i ))) with ks and θs chosen accordingly data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

35. 35 Costs & Thresholds  Costs: where from?  “Expert” knowledge  Derive from data (might be tricky)  Thresholds: where from?  Fixed  Actually optimize at the same time SmoothT2 = SmoothT2(w, th1, th2) Differentiable in th1 and th2, so we can do gradient search for it Calibrates in one step both the belief updating and the threshold to minimize loss data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

36. 36 Questions: What Next?  ICT: can we do anything there?  Looks really tough  Push for better performance  … Add more features?  … Debug the models more, eliminate singularities  … Why doesn’t the model-tree do better?  Push for better understanding  … What are the other interesting questions …  Optimize for new loss function  More in the future: look at the full belief updating problem data : problem/approach : user behaviors : preliminary results : more on evaluation : what next?

37. 37 Thank You!

38. 38 Encoding System Actions  For each concept update, define system action signature:  IC: Implicit Confirm [grounding]  ICT: Implicit Confirm [task]  EC: Explicit Confirm  REQ: Request  Each variable can have 1 of 4 values  0  C (action happens on concept of interest)  OC (action happens on some other concept)  C&OC (action happens both on concept of interest and some other concept)  Only certain combinations are valid and appear in the data