Towards Learning Dialogue Structures from Speech Data and Domain Knowledge: Challenges to Conceptual Clustering using Multiple and Complex Knowledge Source

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Towards Learning Dialogue Structures from Speech
Data and Domain Knowledge Challenges to
Conceptual Clustering using Multiple and

Complex Knowledge Source

• Jens-Uwe Moller
• Natural Language Systems Division,
• Dept. of Computer Science, Univ. of Hamburg

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Overview

• Dialog modeling based on a set of units called
  dialog act
• Dialog acts from theory doesnt fit with a
  specific domain
• Labeling dialog is time consuming and subjective
• learn an application specific dialog acts from
  speech data using conceptual clustering

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The learning task

• Learning dialog acts from turns
• Unsupervised classification (no prior definition
  of dialog acts is given)
• Hierarchy classification with inspectable
  classifying rules

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Features

• Domain knowledge structure of task, task
  knowledge represented by goals and plans
• Word recognizer word hypotheses
• Prosodic data Pause Stress mark important unit
• Lexical semantics
• Syntax (less important in spoken dialog)
• Semantics (larger units of lexical semantics)

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COWEB

• Symbolic machine learning algorithm
• Build a classification tree
• Distinction between subnodes are made from a
  function overall attribute
• Support probabilistic data
• Support multiple overlapping hierarchies (for
  ambiguous case)
• Can handle multiple entries of one attribute
  (e.g. stream of words)

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COWEB (2)

• Learning from simultaneous events
• Learn from structure data Conceptual Graphs.
• Learn case descriptions from terminological
  descriptions
• Subsumption correclation criterion over
  structured data. e.g. subsumption of individuals
  to classes

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Metrics for Measuring Domain Independence of
Semantic Classes

• Andrew Pargellis, Eric Fosler-Lussier, Alexandros
  Potamianos, Chin-Hui LeeDialogue Systems
  Research Dept., Bell Labs, Lucent Technologies
  Murray Hill, NJ, USA

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Introduction

• Employ semantic classes (concepts) from another
  domain
• Need to identify domain-independent concepts base
  on comparison across domain
• Domain-independent concepts should occur in
  similar syntactic (lexical) contexts across
  domains

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Comparing concepts across domains

• Concept-comparison method
• Concept-projection method

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Concept-comparison method

• Find the similarity between all pairs of concepts
  across the two domains
• Two concepts are similar if their respective
  bigram contexts are similar
• Use left and right context bigram language models

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Kullback-Leibler (KL) distance

• Compare how san francisco and newark are used in
  the Travel domain with how comedies and westerns
  are used in the Movie domain
• Distance between two concepts

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Concept-projection method

• How well a single concept from one domain is
  represented in another domain.
• How the words comedies and westerns are used in
  both domains
• Useful for identifying the degree of
  domain-independence for a particular concept.

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Result Concept-comparison
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Result Concept-projection
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Concept Example
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Semi-Automatic Acquisition of Domain-Specific
Semantic Structures

• Siu K.C., Meng H.M.
• Human-Computer Communications Laboratory
• Department of Systems Engineering
• and Engineering Management
• The Chinese University of Hong Kong

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Grammar induction

• Use unannotated corpora
• Portable across domain language
• Output grammar has reasonable coverage of
  within-domain data and reject out-of-domain data
• Amenable to interactive refinement by human
• Support optional injection of prior knowledge

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Spatial clustering

• Use kullback-liebler distance.
• use left and right context.
• Consider word with pre-set minimum occurrence.
  (set to 5)
• use left and right context. Consider word w1, w2
  (later be c1, c2) pair-wise for words that have a
  least pre-set minimum occurrence. (set to 5)

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Temporal clustering

• Use Mutual Information (MI).
• N-highest MI pairs are clustered (N5 in
  experiment)
• Do spatial clustering and temporal clustering
  iteratively
• Post-process by human

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Automatic Concept identification
In
goal-oriented conversations

• Ananlada Chotimongkol and Alexander I. Rudnicky
• Language Technologies Institute Carnegie Mellon
  University

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Concept identification

• First step towards the goal of automatically
  inferring domain ontologies
• Goal-oriented human-human conversation has a
  clear structure
• This structure can be used to automatically
  identify domain topics, e.g. dialog classfication

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Clustering algorithm

• Hierarchical clustering
• Mutual information based
• Criterionminimize the loss of average mutual
  information
• Kullback-Lierbler based
• Criterionword pair with minimum distance

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Evaluation metrics

• Reference concept from class-based n-gram model
• Cluster conceptmajority concept
• Precision
• Recall
• Singularity score (SS)
• Quality score (QS)