misunderstandings, corrections and beliefs in spoken language interfaces

1
misunderstandings, corrections and beliefs in
spoken language interfaces

• Dan Bohus Computer Science Department
• www.cs.cmu.edu/dbohus Carnegie Mellon
  University
• dbohus_at_cs.cmu.edu Pittsburgh, PA 15213

2
problem

• spoken language interfaces lack robustness when
  faced with understanding errors

• stems mostly from speech recognition
• spans most domains and interaction types
• exacerbated by operating conditions

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more concretely

• S What city are you leaving from?
• U Urbana Champaign OKAY IN THAT SAME PAY
• S Sorry Im not sure I understood what you said.
  What city are you leaving from ?
• U Urbana Champaign FOR MINUTE SINCE HEY
• S Im still having trouble understanding you. To
  go on, I need you to answer the following
  question. What city are you leaving from?
• U Chicago CHICAGO
• S traveling from Chicago. Where would you like
  to go?
• U Huntsville SEOUL
• S traveling to Seoul. What day did you need to
  travel?
• U no no Im traveling to Birmingham THE
  TRAVELING TO BERLIN P_M
• S traveling in the afternoon. Okay what day
  would you be departing chicago
• U /uh/ the tenth of august AT THE TENTH OF
  AUGUST
• S a flight tomorrow I have a Korean Airlines
  flight departing Chicago at 140pm, arrives Seoul
  at 5pm the next day. Do you want to take that?
• U my destination is Birmingham FLIGHT
  DESTINATION MR WEEKEND
• S traveling on Saturday, August 12th I have a
  flight departing Chicago at 140pm arrives Seoul
  at

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some statistics

• semantic error rates 25-35

SpeechActs SRI 25
CU Communicator CU 27
Jupiter MIT 28
CMU Communicator CMU 32
How May I Help You? ATT 36

• corrections Krahmer, Swerts, Litman, Levow
• 30 of utterances correct system mistakes
• 2-3 times more likely to be misrecognized

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two types of understanding errors
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misunderstandings

• fix recognition

• detect potential misunderstandings do something
  about them

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outline

• detecting misunderstandings
• detecting user correctionslate-detection of
  misunderstandings
• belief updatingconstruct accurate beliefs by
  integrating information from multiple turns

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detecting misunderstandings

• recognition confidence scores

S What city are you leaving from? U Birmingham
BERLIN PM
conf0.63

• traditionally Bansal, Chase, Cox, Kemp, many
  others
• speech recognition confidence scores
• use acoustic, language model and search info
• frame, phoneme, word-level

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semantic confidence scores

• were interested in semantics, not words
• YES YEAH, NO NO WAY
• use machine learning to build confidence
  annotators
• in-domain, manually labeled data
• utterance BERLIN PM Birmingham
• labels correct / misunderstood
• features from different knowledge sources
• binary classification problem
• probability of misunderstanding regression
  problem

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a typical result

• Identifying User Corrections Automatically in a
  Spoken Dialog System Walker, Wright, Langkilde
• HowMayIHelpYou corpus call routing for phone
  services
• 11787 turns
• features
• ASR recog, numwords, duration, dtmf, rg-grammar,
  tempo
• understanding confidence, context-shift,
  top-task, diff-conf,
• dialog history sys-label, confirmation,
  num-reprompts, num-confirms, num-subdials,
• binary classification task
• majority baseline (error) 36.5
• RIPPER (error) 14

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outline

• detecting misunderstandings
• detecting user corrections
• late-detection of misunderstandings
• belief updating
• construct accurate beliefs by integrating
  information from multiple turns

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detect user corrections

• is the user trying to correct the system?

S Where would you like to go? U Huntsville
SEOUL S traveling to Seoul. What day did you
need to travel? U no no Im traveling to
Birmingham THE TRAVELING TO BERLIN P_M
misunderstanding
user correction
misunderstanding

• same story use machine learning
• in-domain, manually labeled data
• features from different knowledge sources
• binary classification problem
• probability of correction regression problem

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typical result

• Identifying User Corrections Automatically in a
  Spoken Dialog System Hirschberg, Litman, Swerts
• TOOT corpus access to train information
• 2328 turns, 152 dialogs
• features
• prosodic f0max, f0mn, rmsmax, dur, ppau, tempo
• ASR gram, str, conf, ynstr,
• dialog position diadist
• dialog history preturn, prepreturn, pmeanf
• binary classification task
• majority baseline 29
• RIPPER 15.7

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outline

• detecting misunderstandings
• detecting user correctionslate-detection of
  misunderstandings
• belief updatingconstruct accurate beliefs by
  integrating information from multiple turns

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belief updating problem an easy case
S on which day would you like to travel? U on
September 3rd AN DECEMBER THIRD CONF0.25
departure_date Dec-03/0.25
S did you say you wanted to leave on December
3rd?
U no
NO CONF0.88
departure_date Ø
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belief updating problem a trickier case
S Where would you like to go? U Huntsville SEO
UL CONF0.65
destination seoul/0.65
S traveling to Seoul. What day did you need to
travel?
U no no Im traveling to Birmingham
THE TRAVELING TO BERLIN P_M CONF0.60
COR0.35
destination ?
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belief updating problem formalized
destination seoul/0.65
S traveling to Seoul. What day did you need to
travel?

• given
• an initial belief Pinitial(C) over concept C
• a system action SA
• a user response R
• construct an updated belief
• Pupdated(C) ? f (Pinitial(C), SA, R)

THE TRAVELING TO BERLIN P_M CONF0.60
COR0.35
destination ?
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outline

• detecting misunderstandings
• detecting user corrections
• late-detection of misunderstandings
• belief updating
• construct accurate beliefs by integrating
  information from multiple turns

• current solutions
• a restricted version
• data
• user response analysis
• experiments and results
• discussion. caveats. future work

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belief updating current solutions

• most systems only track values, not beliefs
• new values overwrite old values
• explicit confirm yes ? trust hypothesis
• explicit confirm no ? kill hypothesis
• explicit confirm other ? non-understanding
• implicit confirm not much
• users who discover errors through incorrect
  implicitconfirmations have a harder time getting
  back on track
• Shin et al, 2002

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outline

• detecting misunderstandings
• detecting user corrections
• late-detection of misunderstandings
• belief updating
• construct accurate beliefs by integrating
  information from multiple turns

• current solutions
• a restricted version
• data
• user response analysis
• experiments and results
• discussion. caveats. future work

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belief updating general form

• given
• an initial belief Pinitial(C) over concept C
• a system action SA
• a user response R
• construct an updated belief
• Pupdated(C) ? f (Pinitial(C), SA, R)

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restricted version 2 simplifications

• compact belief
• system unlikely to hear more than 3 or 4 values
• single vs. multiple recognition results
• in our data max 3 values, only 6.9 have gt1
  value
• confidence score of top hypothesis
• updates after confirmation actions
• reduced problem
• ConfTopupdated(C) ? f (ConfTopinitial(C), SA, R)

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outline

• detecting misunderstandings
• detecting user corrections
• late-detection of misunderstandings
• belief updating
• construct accurate beliefs by integrating
  information from multiple turns

• current solutions
• a restricted version
• data
• user response analysis
• experiments and results
• discussion. caveats. future work

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data

• collected with RoomLine
• a phone-based mixed-initiative spoken dialog
  system
• conference room reservation
• search and negotiation
• explicit and implicit confirmations
• confidence threshold model ( some exploration)
• implicit confirmation task

• I found 10 rooms for Friday between 1 and 3 p.m.
  Would like a small room or a large one?

• I found 10 rooms for Friday between 1 and 3 p.m.
  Would like a small room or a large one?

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user study

• 46 participants, 1st time users
• 10 scenarios, fixed order
• presented graphically (explained during briefing)

• compensated per task success

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corpus statistics

• 449 sessions, 8848 user turns
• orthographically transcribed
• manually annotated
• misunderstandings (concept-level)
• non-understandings
• user corrections
• correct concept values

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outline

• detecting misunderstandings
• detecting user corrections
• late-detection of misunderstandings
• belief updating
• construct accurate beliefs by integrating
  information from multiple turns

• current solutions
• a restricted version
• data
• user response analysis
• experiments and results
• discussion. caveats. future work

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user response types

• following Krahmer and Swerts
• study on Dutch train-table information system
• 3 user response types
• YES yes, right, thats right, correct, etc.
• NO no, wrong, etc.
• OTHER
• cross-tabulated against correctness of
  confirmations

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user responses to explicit confirmations

• from transcripts
• numbers in brackets from KrahmerSwerts
• from decoded

YES NO Other
CORRECT 94 93 0 0 5 7
INCORRECT 1 6 72 57 27 37
YES NO Other
CORRECT 87 1 12
INCORRECT 1 61 38
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other responses to explicit confirmations

• 70 users repeat the correct value
• 15 users dont address the question
• attempt to shift conversation focus

User does not correct User corrects
CORRECT 1159 0
INCORRECT 29 10 of incor 250 90 of incor
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user responses to implicit confirmations

• transcripts
• numbers in brackets from KrahmerSwerts
• decoded

YES NO Other
CORRECT 30 0 7 0 63 100
INCORRECT 6 0 33 15 61 85
YES NO Other
CORRECT 28 5 67
INCORRECT 7 27 66
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ignoring errors in implicit confirmations
User does not correct User corrects
CORRECT 552 2
INCORRECT 118 51 of incor 111 49 of incor

• users correct later (40 of 118)
• users interact strategically
• correct only if essential

correct later correct later
critical 55 2
critical 14 47
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outline

• detecting misunderstandings
• detecting user corrections
• late-detection of misunderstandings
• belief updating
• construct accurate beliefs by integrating
  information from multiple turns

• current solutions
• a restricted version
• data
• user response analysis
• experiments and results
• discussion. caveats. future work

34
machine learning approach

• need good probability outputs
• low cross-entropy between model predictions and
  reality
• cross-entropy negative average log posterior
• logistic regression
• sample efficient
• stepwise approach ? feature selection
• logistic model tree for each action
• root splits on response-type

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features. target.

• initial situation
• initial confidence score
• concept identity, dialog state, turn number
• system action
• other actions performed in parallel
• features of the user response
• acoustic / prosodic features
• lexical features
• grammatical features
• dialog-level features
• target was the value correct?

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baselines

• initial baseline
• accuracy of system beliefs before the update
• heuristic baseline
• accuracy of heuristic rule currently used in the
  system
• oracle baseline
• accuracy if we knew exactly when the user is
  correcting the system

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results explicit confirmation
Hard error ()
Soft error
38
results implicit confirmation
Hard error ()
Soft error
39
results unplanned implicit confirmation
Hard error ()
Soft error
40
informative features

• initial confidence score
• prosody features
• barge-in
• expectation match
• repeated grammar slots
• concept id
• priors on concept values not included in these
  results

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outline

• detecting misunderstandings
• detecting user corrections
• late-detection of misunderstandings
• belief updating
• construct accurate beliefs by integrating
  information from multiple turns

• current solutions
• a restricted version
• data
• user response analysis
• experiments and results
• discussion. caveats. future work

42
discussion

• evaluation
• does it make sense?
• what would be a better evaluation?
• current limitation belief compression
• extending models to N hypothesis other
• current limitation system actions
• extending models to cover all system actions

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thank you!
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a more subtle caveat

• distribution of training data
• confidence annotator heuristic update rules
• distribution of run-time data
• confidence annotator learned model
• always a problem when interacting with the world!
• hopefully, distribution shift will not cause
  large degradation in performance
• remains to validate empirically
• maybe a bootstrap approach?

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KL-divergence cross-entropy

• KL divergence D(pq)
• Cross-entropy CH(p, q) H(p) D(pq)
• Negative log likelihood

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logistic regression

• regression model for binomial (binary) dependent
  variables

• fit a model using max likelihood (avg
  log-likelihood)
• any stats package will do it for you
• no R2 measure
• test fit using likelihood ratio test
• stepwise logistic regression
• keep adding variables while data likelihood
  increases signif.
• use Bayesian information criterion to avoid
  overfitting

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logistic regression
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logistic model tree

• regression tree, but with logistic models on
  leaves

f
f0
f1
g
ggt10
glt10