Sorry, I didn’t catch that … Non-understandings and recovery in spoken dialog systems Part II: Sources & impact of non-understandings, Performance of various

Sorry, I didn’t catch that …Non-understandings and recovery in spoken dialog systemsPart II: Sources & impact of non-understandings, Performance of various recovery strategies

Dan BohusSphinx Lunch TalkCarnegie Mellon University, March 2005

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Non-understandings

S: What city are you leaving from?U: Urbana Champaign [OKAY IN THAT SAME PAY]

NON-understanding

System cannot extract any meaningful information from the user’s turn

How can we prevent non-understandings? How can we recover from them?

Detection Set of recovery strategies Policy for choosing between them

review : sources : impact : strategy performance

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Issues under investigation

Data Collection Detection / Diagnosis

What are the main causes (sources) of non-understandings? What is their impact on global performance? Can we diagnose non-understandings at run-time? Can we optimize the rejection process in a more principled way?

Set of recovery strategies What is the relative performance of different recovery strategies? Can we refine current strategies and find new ones

Policy for choosing between them Can we improve performance by making smarter choices? If so, can we learn how to make these smarter choices?


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Data Collection: Experimental Design

Subjects interact over the telephone with RoomLine Performed 10 of scenario-based tasks

Between-subjects experiment, 2 groups: Control: system uses a random (uniform) policy for engaging

the non-understanding recovery strategies Wizard: policy is determined at runtime by a human (wizard)

46 subjects, balanced gender x native 449 sessions; 8278 user turns Sessions transcribed & annotated


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REPROMPT

NOTIFY

MOVE-ON

HELP

REPEAT

Non-understanding StrategiesS: For when do you need the room?U: [non-understanding]

1. MOVE-ON (MOVE) Sorry, I didn’t catch that. For which day you need the room?

2. YOU CAN SAY (YCS) Sorry, I didn’t catch that. For when do you need the conference room? You can say something like tomorrow at 10 am …3. TERSE YOU CAN SAY (TYCS) Sorry, I didn’t catch that. You can say something like tomorrow at 10 am …

4. FULL HELP (HELP) Sorry, I didn’t catch that. I am currently trying to make a conference room reservation for you. Right now I need to know the date and time for when you need the reservation. You can say something like tomorrow at 10 am …5. ASK REPEAT (AREP) Could you please repeat that?6. ASK REPHRASE (ARPH) Could you please try to rephrase that?7. NOTIFY (NTFY) Sorry, I didn’t catch that ...8. YIELD TURN (YLD) …9. REPROMPT (RP) For when do you need the conference room?

10. DETAILED REPROMPT (DRP) Right now I need to know the date and time for when you need the reservation …


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Issues under Investigation






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Communication [Clark, Horvitz, Paek]

ConversationLevel

IntentionLevel

SignalLevel

ChannelLevel

Channel

Recognition

Parsing

Interpretation

End-pointing

Goal

Semantics

Text

Audio

User System


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Modeling and Breakdowns

ConversationLevel

IntentionLevel

SignalLevel

ChannelLevel

Channel

Recognition

Parsing

Interpretation

End-pointing

Goal

Semantics

Text

Audio

User System


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“Location” & “types” of errors

Channel

Recognition

Parsing

Interpretation

End-pointing

Goal

Semantics

Text

Audio

User SystemOut-of-domain

Out-of-applicationFalse Rejections

Out-of-grammarOut-of-relevance

ASR errorsaccents

noises


End-pointer errors

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% of non-understandings


ASR errorsaccents

noises

12.89%

18.59%

8.02%

3.21%

56.05%

3.91%

Out-of-domainOut-of-applicationFalse Rejections

0.14%


End-pointer errors

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Out-of-application (13% of Nonu)

2 main classes, about equally split Request for inexistent task functionality

“A room Monday or Tuesday” “do you have anything anytime Thursday afternoon?”

Request for inexistent “meta” functionality Corrections:

“Can I change the date” “You got the time wrong” “Wrong day”

Q: How to better convey system boundaries? Q: Extend system language for corrections?


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Out-of-grammar (8% of Nonu)

Imperfect grammar coverage “Doesn’t matter” “It doesn’t matter” “Internet connection” “Network connection” “Vaguely” “So so” / “Generally” / etc

Q: Bring users in grammar? Carefully craft & use the “You Can Say” prompts

Q: Extend the grammar? Online & in an unsupervised fashion?


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Grammaticality - Summary

It’s important: 25% of non-understandings Stems (about equally) from:

Requests for inexistent task functionality Requests for inexistent meta/corrections functionality Lack of grammar coverage

Solutions Offline: enlarge grammar, include correction language Online

Carefully design “You Can Say” All You Can Say [Collagen / USI] Unsupervised learning of new grammar expressions


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All You Can Say

How much of the system functionality is actually used? [under work] Certain “task” and “meta” aspects of functionality are very

rarely or never used

User System

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% of non-understandings


ASR errorsaccents

noises

12.89%

18.59%

8.02%

3.21%

56.05%

3.91%

Out-of-domainOut-of-applicationFalse Rejections

0.14%


End-pointer errors

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Impact on system performance

Logistic regression model Task Success % Non-understandings per session

Natives are more likely to succeed at the same non-understandings rate (Participants in the wizard condition also)

2nd model (also use Misunderstandings) Task success % Non + % Mis Better fit Adding native information does not improve model Non-u on average half as costly


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Set of recovery strategies What is the relative performance of different recovery strategies? Can we refine current strategies and find new ones?



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REPROMPT

NOTIFY

MOVE-ON

HELP

REPEAT

Non-understanding StrategiesS: For when do you need the room?U: [non-understanding]

1. MOVE-ON (MOVE) Sorry, I didn’t catch that. For which day you need the room?

2. YOU CAN SAY (YCS) Sorry, I didn’t catch that. For when do you need the conference room? You can say something like tomorrow at 10 am …3. TERSE YOU CAN SAY (TYCS) Sorry, I didn’t catch that. You can say something like tomorrow at 10 am …

4. FULL HELP (HELP) Sorry, I didn’t catch that. I am currently trying to make a conference room reservation for you. Right now I need to know the date and time for when you need the reservation. You can say something like tomorrow at 10 am …5. ASK REPEAT (AREP) Could you please repeat that?6. ASK REPHRASE (ARPH) Could you please try to rephrase that?7. NOTIFY (NTFY) Sorry, I didn’t catch that ...8. YIELD TURN (YLD) …9. REPROMPT (RP) For when do you need the conference room?

10. DETAILED REPROMPT (DRP) Right now I need to know the date and time for when you need the reservation …


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How to evaluate performance?

Recovery Next turn is okay (not a non-understanding, not a

misunderstanding)

Finer-grained recovery Next turn CER Next turn concept transfer (dialog cost)

Time (+recovery) ?? Time lost: 0 if next turn okay, time lost otherwise Time to recovery (has some problems) [More stuff under construction]


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Which strategies are better?


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Recovery performance ranked list, based on pair-wise t-tests:

RNK MOVE HELP TYCS RP YCS ARPH DRP NTFY AREP YLD

MOVE 1 MOVE: - - - 1.31 1.33 1.35 1.71 1.8 1.91 2.06

HELP 2 HELP: - - - - - - 1.55 1.64 1.73 1.87

HELP 3 TYCS: - - - - - - 1.5 1.58 1.68 1.81

SIG 4 RP: - - - - - - - - 1.46 1.58

HELP 5 YCS: - - - - - - - - 1.44 1.55

SIG 6 ARPH: - - - - - - - - 1.42 1.53

SIG ? DRP: - - - - - - - - - -

SIG ? NTFY: - - - - - - - - - -

SIG ? AREP: - - - - - - - - - -

SIG ? YLD: - - - - - - - - - -

CER evaluation shows similar results


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MoveOn ≥ Help > Signal

RANK MOVE C1_HELP C1_SIG

1 MOVE - 1.19* 1.65

2 C1_HELP - - 1.38

3 C1_SIG - - -

* p = 0.1089


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What is the Impact on User Response?

Labeled user responses in 5 classes:[same tagging scheme as Shin, Choularton]

Answer (1st) Repeat Rephrase Change Contradict Other Hang-up


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What is the Impact on User Response?

Labeled user responses in 5 classes:[same tagging scheme as Shin, Choularton]


17.95%

44.30%

30.70%

3.63%

3.13%


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Comparing with other systems


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What responses are the best?

Recovery as a function of response type


45.45%

39.33%

63.29%

19.05%


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More to come …

Per-strategy analysis Barge-in & impact on recovery


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Refining the current set of strategies

Introduce more alternative dialog plans opportunities for Move-On

“You Can Say” Carefully tune the prompts Smarter barge-in control “All You Can Say”

“Speak shorter” Anecdotal evidence to be corroborated by analysis

“Speak louder / go to a quieter place” Not so much in these experiments, but evidence from

Let’s go! More prevention measures

If someone has troubles, you can give the YCS prompts without waiting for a non-understanding to happen


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Thank You!!






Documents

Sorry, I didn’t catch that … Non-understandings and recovery in spoken dialog systems Part II: Sources & impact of non-understandings, Performance of various