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Modelling Language EvolutionLecture 5: Iterated Learning
Simon Kirby
University of Edinburgh
Language Evolution & Computation Research Unit
Models so far…
Models of learning language Models of evolving ability to learn language Models of differing abilities to learn differing languages
What do these have in common? The language comes from “outside”
LINGUISTICAGENT
LANGUAGE
Neural network
TrainingSentences
Weightsettings
Two kinds of models
Language Acquisition Device
Primary Linguistic Data
Grammatical Competence
What can be learned?
What can evolve?
LADPLD GCLADPLD GC LADPLD GC
LADPLD GC LADPLD GCLADPLD GC
LADPLD GC
LADPLD GC
LADPLD GC
A new kind of model: Iterated Learning
LADPLD GCLADPLD GC LADPLD GC
LADPLD GC
LADPLD GC
LADPLD GC
LADPLD GC
LADPLD GCLADPLD GC
What kind of language evolves?
What can Iterated Learning explain?
My hypothesis: some functional linguistic structure emerges inevitably from the process of iterated learning without the need for natural selection or explicit functional pressure.
First target structure:
Recursive Compositionality: the meaning of an utterance is some function of the meaning of parts of that utterance and the way they are put together.
Compositional Holistic
walked went
I greet you Hi
I thought I saw a pussy cat chutter
The agent
Meaning-signal Pairs in(utterances from parent)
Meaning-signal Pairs out(to next generation)
Meanings(generated by environment)
Learning Algorithm
Internal linguistic representation
Agent(simulated individual)
Production AlgorithmNe
xt g
ene
ratio
n
What will the agents talk about?
Need some simple but structured “world”. Simple predicate logic:
Agents can string random characters together to form utterances.
loves(mary, john)admires(gavin, heather)
thinks(mary, likes(john, heather))
knows(heather, thinks(mary, likes(john, heather)))
How do agents learn?
Not using neural networksIn this model, interested in more traditional, symbolic
grammarsLearners try and form a grammar that is consistent
with the primary linguistic data they hear.Fundamental principle: learning is compression.
Learners try and fit data heard, but also generaliseLearning is a trade-off between these two
Two steps to learning
aryjohnlovesm),(/ maryjohnlovesS
aryjohnlovesm
marypeterloves
),(/
),(/
maryjohnlovesS
marypeterlovesS
peter
john
lovesmary
peterC
johnC
xCmaryxlovesS
/
/
/),(/
INCORPORATION (for each sentence heard)
GENERALISATION (whenever possible)
A simulation run
1. Start with one learner and one adult speaker neither of which have grammars.
2. Choose a meaning at random.
3. Get speaker to produce signal for that meaning (may need to “invent” random string).
4. Give meaning-signal pair to learner.
5. Repeat 2-4 one hundred and fifty times.
6. Delete speaker.
7. Make learner be the new speaker.
8. Introduce a new learner (with no initial grammar)
9. Repeat 2-8 thousands of times.
Results 1a: initial stages
Initially, speakers have no language, so “invent” random strings of characters.
A protolanguage emerges for some meanings, but no structure. These are holistic expressions:
1. ldg “Mary admires John”
2. xkq “Mary loves John”
3. gj “Mary admires Gavin”
4. axk “John admires Gavin”
5. gb “John knows that Mary knows that John admires Gavin”
Results 1b: many generations later…
6. gj h f tej m John Mary admires“Mary admires John”
7. gj h f tej wp John Mary loves“Mary loves John”
8. gj qp f tej m Gavin Mary admires“Mary admires Gavin”
9. gj qp f h m Gavin John admires“John admires Gavin”
10. i h u i tej u gj qp f h m John knows Mary knows Gavin John admires“John knows that Mary knows that John admires Gavin”
What’s going on?
There is no biological evolution in the ILM.There isn’t even any communication; no notion of
function in model at all.So, why are structured languages evolving?Hypothesis:
Languages themselves are evolving to the conditions of the ILM in order that they are learnable.
The agents never see all the meanings…Only rules that are generalisable from limited exposure
are stable.
Language has to fit through a narrow bottleneck
This has profound implications for the structure of language
Linguistic competence
Linguistic performance
Linguistic competence
Production
Learning
A nice and simple model…
Language Meanings: 8-bit binary numbers Signals: 8-bit binary numbers
Agents 8x8x8 neural network (not SRN) Learns to associate signals to meanings
SIGNALS
MEANINGS
Bottleneck
Only one parameter in this model The bottleneck:
The number of meaning-signal pairs (randomly chosen) given to the next generation…
In each simulation, we can measure two things: Expressivity: the proportion of the
meaning-space an adult agent can give a unique signal to
Instability: how different each generation’s language is to that of the previous generation
Subset of meaning signal pairs
Subset of meaning signal pairs
Adaptation
Language is evolving to be learnable
Structure in mapping emerges Meanings and signals are related by
simple rules of bit flipping and re-ordering
These rules can be learned from a subset
Despite the hugely different model, this is a very similar result to the earlier simulation
Summary
Language is learned by individuals with innate learning biases
The language data an individual hears is itself the result of learning
Languages adapt through iterated learning in response to our innate biases
There’s more! Our learning biases adapt through
biological evolution in response to the language we use
Tomorrow… use a simulation package to look at “grounding” models in an environment
Culturalevolution
Individual learning
Biological evolution