Bayesian models of crosssituational word learning

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Bayesian models of cross-situational word
learning
Michael C. Frank Noah Goodman Josh Tenenbaum (MIT)
Thanks to Kathy Hirsh-Pasek and Roberta Golinkoff
for valuable discussion. Also thanks to Vikash
Mansinghka, Ted Gibson, tedlab, and cocosci for
comments and the Jacob Javits Foundation for
funding.
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Word-learning in action
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The problem of word learning
In any one situation, children hear many words
and see many objects
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One possible solution
Apply a cross-situational strategy to learn
mappings (but this is harder than it looks)
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The problem of word learning

• Techniques for cross-situational word learning
• Deductive inference Siskind (1996)
• Translation model Yu, Ballard, Aslin (2005),
  Yu Ballard (in press)

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Outline

• Some facts of word learning
• Mutual exclusivity
• Fast-mapping
• Use of social cues
• Our model Bayesian word-learner
• Extension Learning social cues
• Experimental coverage

• Some facts of word learning
• Our model Bayesian word-learner
• Extension Learning social cues
• Experimental coverage

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Three facts of word learning
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Outline

• The facts of word learning
• Our model Bayesian word-learner
• Model
• Corpus
• Comparison models
• Results
• Extension Learning social cues
• Experimental coverage

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Generative model
objects
O
lexicon
things you intend to refer to
I
W
words
? situations
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Generative model example
? situations
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Inference
Bayes rule
Parsimony prior on lexicons

• Inference technique
• Stochastic search with simulated tempering
• Data-driven proposals drawn from the mutual
  information of word-object pairings

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Corpus

• 2x10 min clips from CHILDES-Rollins
• Interaction between mom and infant (6mo)
• 2528 word tokens of 420 words in 623 sentences
• 24 objects, all toys

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Model comparison

• Co-occurrence frequency
• Point-wise mutual information

• Translation model, based on IBM model 1 (Yu
  Ballard, in press)

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Results model comparison
recall
precision
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Results intuitive analysis
Best lexicon found by search
Most likely intentions
Also unlike baseline models, our model is
extremely extensible
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Outline

• The facts of word learning
• Our model Bayesian word-learner
• Extension Learning social cues
• Corpus
• Model
• Preliminary results
• Experimental coverage

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Social corpus coding

• Coded social cues for each utterance infants
  hands, eyes, mouth, and touch moms hands, eyes,
  and touch

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How it works
could be caused by base rate or by relevance
Noisy OR process
base rate
relevance
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Social model framework
objects
O
lexicon
relevance and base rate of social cues
things you intend to refer to
r,b
I
S
W
social cues
words
? situations
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Preliminary Results
Model finds appropriate features
Social features allow finding intent in
situations without referential words
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Outline

• The facts of word learning
• Our model Bayesian word-learner
• Extension Learning social cues
• Experimental coverage
• Mutual exclusivity
• Fast-mapping
• Use of social cues

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Mutual exclusivity
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Fast-mapping
model can fast-map learn a word from a single
instance
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Use of social cues
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Conclusions

• Bayesian model of cross-situational word-learning
• Performed best over a corpus
• Allows parsing of sentences and interpretation of
  speakers intent
• Social model
• Model can learn which social cues are relevant to
  reference
• Experimental coverage
• Mutual exclusivity
• Fast-mapping
• Learning words for social cues