online supervised learning of non-understanding recovery policies Dan Bohus Computer Science Department Carnegie Mellon University Pittsburgh, PA with thanks to: Alex Rudnicky Brian Langner Antoine Raux Alan Black Maxine Eskenazi

2 Sorry, I didn’t catch that … Can you repeat that? Can you rephrase that? Where are you flying from? Please tell me the name of the city you are leaving from … Could you please go to a quieter place? Sorry, I didn’t catch that … tell me the state first … S: understanding-errors in spoken dialog S: Where are you flying from? U: Birmingham [BERLIN PM] System constructs an incorrect semantic representation of the user’s turn MIS-understanding S: Where are you flying from? U: Urbana Champaign [OKAY IN THAT SAME PAY] System fails to construct a semantic representation of the user’s turn NON-understanding Did you say Berlin? from Berlin … where to? S: ? ? ?

3 recovery strategies  large set of strategies (“strategy” = 1-step action)  tradeoffs not well understood  some strategies are more appropriate at certain times  OOV -> ask repeat is not a good idea  door slam -> ask repeat might work well Sorry, I didn’t catch that … Can you repeat that? Can you rephrase that? Where are you flying from? Please tell me the name of the city you are leaving from … Could you please go to a quieter place? Sorry, I didn’t catch that … tell me the state first … S:

4 recovery policy  “policy” = method for choosing between strategies  difficult to handcraft  especially over a large set of recovery strategies  common approaches  heuristic  “three strikes and you’re out” [Balentine] 1 st non-understanding: ask user to repeat 2 nd non-understanding: provide more help, including examples 3 rd non-understanding: transfer to an operator

5 this talk … … an online, supervised method for learning a non-understanding recovery policy from data

6 overview  introduction  approach  experimental setup  results  discussion

7 overview  introduction  approach  experimental setup  results  discussion

8 intuition … … if we knew the probability of success for each strategy in the current situation, we could easily construct a policy S: Where are you flying from? U: [OKAY IN THAT SAME PAY] Urbana Champaign Sorry, I didn’t catch that … Can you repeat that? Can you rephrase that? Where are you flying from? Please tell me the name of the city you are leaving from … Could you please go to a quieter place? Sorry, I didn’t catch that … tell me the state first … S: 32% 15% 20% 30% 45% 25% 43%

9 two step approach step 1: learn to estimate probability of success for each strategy, in a given situation step 2: use these estimates to choose between strategies (and hence build a policy)

10 learning predictors for strategy success  supervised learning: logistic regression  target: strategy recovery successfully or not “success” = next turn is correctly understood labeled semi-automatically  features: describe current situation extracted from different knowledge sources recognition features language understanding features dialog-level features [state, history]

11 logistic regression  well-calibrated class-posterior probabilities  predictions reflect empirical probability of success  x% of cases where P(S|F)=x are indeed successful  sample efficient  one model per strategy, so data will be sparse  stepwise construction  automatic feature selection  provide confidence bounds  very useful for online learning

12 two step approach step 1: learn to estimate probability of success for each strategy, in a given situation step 2: use these estimates to choose between strategies (and hence build a policy)

13 policy learning  choose strategy most likely to succeed  BUT:  we want to learn online  we have to deal with the exploration / exploitation tradeoff S1 S2 S3 S4 0 1

14 highest-upper-bound learning  choose strategy with highest-upper-bound  proposed by [Kaelbling 93]  empirically shown to do well in various problems  intuition S1 S2 S3 S exploitation exploration

15 highest-upper-bound learning  choose strategy with highest upper bound  proposed by [Kaelbling 93]  empirically shown to do well in various problems  intuition S1 S2 S3 S exploitation exploration

16 highest-upper-bound learning  choose strategy with highest upper bound  proposed by [Kaelbling 93]  empirically shown to do well in various problems  intuition S1 S2 S3 S exploitation exploration

17 highest-upper-bound learning  choose strategy with highest upper bound  proposed by [Kaelbling 93]  empirically shown to do well in various problems  intuition S1 S2 S3 S exploitation exploration

18 highest-upper-bound learning  choose strategy with highest upper bound  proposed by [Kaelbling 93]  empirically shown to do well in various problems  intuition S1 S2 S3 S exploitation exploration

19 overview  introduction  approach  experimental setup  results  discussion

20 system  Let’s Go! Public bus information system  connected to PAT customer service line during non-business hours  ~30-50 calls / night

21 strategies Name Example HLPFor instance, you can say ‘FORBES AND MURRAY’, or ‘DOWNTOWN’ HLP_R For instance, you can say ‘FORBES AND MURRAY’, or ‘DOWNTOWN’, or say ‘START OVER’ to restart RPWhere are you leaving from? [repeats previous system prompt] AREPCan you repeat what you just said? ARPHCould you rephrase that? MOVE Tell me first your departure neighborhood … [ignore the current non- understanding and back-off to an alternative dialog plan] ASA Please use shorter answers because I have trouble understanding long sentences … SLLSorry, I understand people best when they speak softer … ITGive general interaction tips to the user ASO I’m sorry but I’m still having trouble understanding you and I might do better if we restarted. Would you like to start over? GUP I’m sorry, but it doesn’t seem like I’m able to help you. Please call back during regular business hours …

22 constraints  constraints  don’t AREP more than twice in a row  don’t ARPH if #words <= 3  don’t ASA unless #words > 5  don’t ASO unless (4 nonu in a row) and (ratio.nonu > 50%)  don’t GUP unless (dialog > 30 turns) and (ratio.nonu > 80%)  capture expert knowledge; ensure system doesn’t use an unreasonable policy  4.2/11 strategies available on average  min=1, max=9

23 features  current non-understanding  recognition, lexical, grammar, timing info  current non-understanding segment  length, which strategies already taken  current dialog state and history  encoded dialog states  “how good things have been going”

24 learning  baseline period [2 weeks, 3/11 -> 3/25, 2006]  system randomly chose a strategy, while obeying constraints  in effect, a heuristic / stochastic policy  learning period [5 weeks, 3/26 -> 5/5, 2006]  each morning labeled data from previous night  retrained likelihood of success predictors  installed in the system for the next night

25 2 strategies eliminated Name Example HLPFor instance, you can say ‘FORBES AND MURRAY’, or ‘DOWNTOWN’ HLP_R For instance, you can say ‘FORBES AND MURRAY’, or ‘DOWNTOWN’, or say ‘START OVER’ to restart RPWhere are you leaving from? [repeats previous system prompt] AREPCan you repeat what you just said? ARPHCould you rephrase that? MOVE Tell me first your departure neighborhood … [ignore the current non- understanding and back-off to an alternative dialog plan] ASA Please use shorter answers because I have trouble understanding long sentences … SLLSorry, I understand people best when they speak softer … ITGive general interaction tips to the user ASO I’m sorry but I’m still having trouble understanding you and I might do better if we restarted. Would you like to start over? GUP I’m sorry, but it doesn’t seem like I’m able to help you. Please call back during regular business hours …

26 overview  introduction  approach  experimental setup  results  discussion

27 results  average non-understanding recovery rate (ANNR)  improvement: 33.6%  37.8% (p=0.03) (12.5%rel)  fitted learning curve: A = B = C = D =

28 policy evolution  MOVE, HLP, ASA engaged more often  AREP, ARPH engaged less often MOVE ASA IT SLL ARPH AREP HLP RP HLP_R

29 overview  introduction  approach  experimental setup  results  discussion

30 are the predictors learning anything?  AREP(653), IT(273), SLL(300)  no informative features  ARPH(674), MOVE(1514)  1 informative feature (#prev.nonu, #words)  ASA(637), RP(2532), HLP(3698), HLP_R(989)  4 or more informative features in the model dialog state (especially explicit confirm states) dialog history

31 more features, more (specific) strategies  more features would be useful  day-of-week  clustered dialog states  ? (any ideas?) ?  more strategies / variants  approach might be able to filter out bad versions  more specific strategies, features ask short answers worked well … speak less loud didn’t … (why?)

32 “noise” in the experiment  ~15-20% of responses following non- understandings are non-user-responses  transient noises  secondary speech  primary speech not directed to the system  this might affect training, in a future experiment we want to eliminate that

33 unsupervised learning  supervised version  “success” = next turn is correctly understood [i.e. no misunderstanding, no non-understanding]  unsupervised version  “success” = next turn is not a non-understanding  “success” = confidence score of next turn  training labels automatically available  performance improvements might still be possible

34 thank you!