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Enhancing Dialogue Systems: Confidence Annotation in the CMU Communicator

This presentation by Dan Bohus and colleagues details the challenges and solutions in ensuring effective communication in spoken dialogue systems. It addresses the common problem of misunderstanding in conversations leading to user frustration and inefficient dialogues. By leveraging confidence annotation and a data-driven approach, the team developed a classifier to accurately label miscommunication instances, analyzing features from dialog logs. The findings emphasize improving error handling and future classifier performance, ultimately aiming to enhance user experience in dialogue systems.

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Enhancing Dialogue Systems: Confidence Annotation in the CMU Communicator

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  1. Is This Conversation on Track? Utterance Level Confidence Annotation in the CMU Communicator spoken dialog system Presented by: Dan Bohus (dbohus@cs.cmu.edu) Work by: Paul Carpenter, Chun Jin, Daniel Wilson, Rong Zhang, Dan Bohus, Alex Rudnicky Carnegie Mellon University – 2001

  2. Outline • The Problem. The Approach • Training Data and Features • Experiments and Results • Conclusion. Future Work Is This Conversation on Track ?

  3. The Problem • Systems often misunderstand, take misunderstanding as fact, and continue to act using invalid information • Repair costs • Increased dialog length • User Frustration • Confidence annotation provides critical information for effective confirmation and clarification in dialog systems. Is This Conversation on Track ?

  4. The Approach • Treat the problem as a data-driven classification task. • Objective: accurately label misunderstood utterances. • Collect a training corpus. • Identify useful features. • Train a classifier ~ identify the best performing one for this task. Is This Conversation on Track ?

  5. Data • Communicator Logs & Transcripts: • Collected 2 months (Oct, Nov 1999). • Eliminated conversations with < 5 turns. • Manually labeled OK (67%) / BAD (33%)BAD ~ RecogBAD / ParseBAD / OOD / NONSpeech • Discarded mixed-label utterances (6%). • Cleaned corpus of 4550 utterances / 311 dialogs. Is This Conversation on Track ?

  6. Feature Extraction 12 Features from various levels: • Decoder Features: • Word Number, Unconfident Percentage • Parsing Features: • Uncovered Percentage, Fragment Transitions, Gap Number, Slot Number, Slot Bigram • Dialog Features: • Dialog State, State Duration, Turn Number, Expected Slots • Garble:handcrafted heuristic currently used by the CMU Communicator Is This Conversation on Track ?

  7. Experiments with 6 different classifiers • Decision Tree • Artificial Neural Network • Naïve Bayes • Bayesian Network • Several network structures attempted • AdaBoost • Individual feature-based binning estimators as weak learners, 750 boosting stages • Support Vector Machines • Dot, Polynomial, Radial, Neural, Anova Is This Conversation on Track ?

  8. Evaluating performance • Classification Error Rate (FP+FN) • CDR = 1-Fallout = 1-(FP/NBAD) • Cost of misunderstanding in dialog systems depends on • Error type (FP vs. FN) • Domain • Dialog state • Ideally, build a cost function for each type of error, and optimize for that Is This Conversation on Track ?

  9. Results – Individual Features • Baseline error 32.84% (when predicting the majority class) • All experiments involved 10-fold cross-validation Is This Conversation on Track ?

  10. Results – Classifiers • T-Test showed there is no statistically significant difference between the classifiers except for the Naïve Bayes • Explanation: independence between feature assumption is violated • Baseline error 25.32% (GARBLE) Is This Conversation on Track ?

  11. Future Work • Improve the classifiers • Additional features • Develop a cost model for understanding errors in dialog systems. • Study/optimize tradeoffs between F/P and F/N; • Integrate value and confidence information to guide clarification in dialog systems Is This Conversation on Track ?

  12. Confusion Matrix • FP = False acceptance • FN = False detection/rejection • Fallout = FP/(FP+TN) = FP/NBAD • CDR = 1-Fallout = 1-(FP/NBAD) Is This Conversation on Track ?

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