Models of Language Evolution Iterated learning Michael Franke
Facets of EvoLang Compositionality Iterated Learning Facets of EvoLang Compositionality Iterated Learning 2 / 24
Facets of EvoLang Compositionality Iterated Learning Facets of EvoLang Compositionality Iterated Learning 7 / 24
Facets of EvoLang Compositionality Iterated Learning Compositional Semantics The meaning of a complex utterance depends systematically on the meaning of its parts and their way of combination. ( 1 ) a. John likes Mary. b. John abhors Mary. c. Mary likes John. 8 / 24
Facets of EvoLang Compositionality Iterated Learning Facets of EvoLang Compositionality Iterated Learning 11 / 24
Facets of EvoLang Compositionality Iterated Learning Iterated Learning — Main Idea • language learners have some domain-general learning capability including a (modest) capacity to generalize and extract patterns • competent speakers have learned from learners . . . . . . who have learned from learners . . . . . . who have learned from learners . . . . . . who have learned from learners . . . ⇒ iterated learning can create structure which wasn’t there before • given capability for generalization • given an appropriately sized “learning bottleneck” 12 / 24
Facets of EvoLang Compositionality Iterated Learning Evolution of Compositionality • 1 learner, 1 teacher • teacher produces n state-signal pairs • learner acquires a language based on these • (iterate:) learner becomes teacher for new learner • learning model: • feed-forward neural network • backpropagation (supervised learning) • production strategy: “obversion” • production optimizes based on individual comprehension (Kirby and Hurford, 2002 ) 13 / 24
Facets of EvoLang Compositionality Iterated Learning Learning Model: Feed-Forward Neural Network • 8 × 8 × 8 network for interpretation • input: signal i = � i 1 , . . . , i 8 � ∈ { 0 , 1 } 8 • output: meaning o = � o 1 , . . . , o 8 � ∈ { 0 , 1 } 8 • initially arbitrary weights 14 / 24
Facets of EvoLang Compositionality Iterated Learning Backpropagation • training items � i , o � are presented • network computes its output o ′ for given i • error δ = o − o ′ is propagated back through all layers • weights are adjusted accordingly picture from http://galaxy.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html 15 / 24
Facets of EvoLang Compositionality Iterated Learning Obverter Strategy • feed-forward net only defines interpretation strategy • production as best choice given the speaker’s own interpretation: • suppose teacher wants to express meaning o ∈ { 0 , 1 } 8 • she then chooses a i c ∈ { 0 , 1 } 8 that triggers network output o ′ ∈ [ 0 , 1 ] 8 if i c maximizes confidence : i c = arg max i ∈{ 0 , 1 } 8 C ( o | i ) defined as: 8 C ( o k | o ′ ∏ C ( o | i ) = k ) k = 1 � o ′ if o k = 1 C ( o k | o ′ k k ) = 1 − o ′ if o k = 0 k 16 / 24
Facets of EvoLang Compositionality Iterated Learning Results ( 20 Trainings Items) dotted: difference teacher-learner language (Kirby and Hurford, 2002 ) solid: proportion of meaning space covered 17 / 24
Facets of EvoLang Compositionality Iterated Learning Results ( 2000 Trainings Items) dotted: difference teacher-learner language (Kirby and Hurford, 2002 ) solid: proportion of meaning space covered 18 / 24
Facets of EvoLang Compositionality Iterated Learning Results ( 50 Trainings Items) dotted: difference teacher-learner language (Kirby and Hurford, 2002 ) solid: proportion of meaning space covered 19 / 24
Facets of EvoLang Compositionality Iterated Learning Compositionality • compositionality arises for medium-sized bottlenecks, e.g.: o 1 = 1 ↔ i 3 = 0 o 2 = 1 ↔ i 5 = 0 o 3 = 1 ↔ i 6 = 0 o 4 = 1 ↔ i 1 = 0 o 5 = 1 ↔ i 4 = 1 o 6 = 1 ↔ i 8 = 1 o 7 = 1 ↔ i 2 = 0 o 8 = 1 ↔ i 7 = 1 20 / 24
Facets of EvoLang Compositionality Iterated Learning Summary • iterated learning “creates” compositional meaning . . . • if bottleneck size is appropriate • by generalizing over sparse training data • by informed innovation (where necessary) • other learning mechanisms possible: • other kinds of neural networks (e.g. Smith et al., 2003 ) • finite state transducers (e.g. Brighton, 2002 ) 21 / 24
Homework solve the mock exam and prepare questions for midterm exam
References Brighton, Henry ( 2002 ). “Compositional Synatx from Cultural Transmission”. In: Artificial Life 8 , pp. 25 – 54 . Kirby, Simon ( 2007 ). “The Evolution of Language”. In: Oxford Handbook of Evolutionary Psychology . Ed. by Robin Dunbar and Louise Barrett. Oxford University Press, pp. 669 – 681 . Kirby, Simon, Tom Griffith, et al. ( 2014 ). “Iterated Learning and the Evolution of Language”. In: Current Opinion in Neurobiology 28 , pp. 108 – 114 . Kirby, Simon and James R. Hurford ( 2002 ). “The Emergence of Linguistic Structure: An Overview of the Iterated Learning Model”. In: Simulating the Evolution of Language . Ed. by A. Cangelosi and D. Parisi. Springer, pp. 121 – 148 . Smith, Kenny et al. ( 2003 ). “Iterated Learning: A Framework for the Emergence of Language”. In: Artificial Life 9 , pp. 371 – 386 .
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