1. Modelling Language Evolution Lecture 3: Evolving Syntax
Simon Kirby
University of Edinburgh
Language Evolution & Computation Research Unit

2. Evolving the ability to learn syntax (Batali 1994)
A “standard” recurrent network does not seem to be able to learn syntax without some help
Elman provides this “help” via incremental memory
The network comes pre-setup to help it learn syntax
i.e., our model of an individual is born with a working memory that grows over time
Does this correspond to an innate prespecification for language learning?

3. Where do innate abilities come from?
If an organism has some innate predisposition…
… and that predisposition is functional, how do we explain it?
Darwinian natural selection seems appropriate.
Could we model natural selection?
Can we evolve a syntax learner? (as opposed to building one by hand?)

4. What things about a network could be innate?
Many features of networks could be thought of as innately determined…
The length of time before context units are blanked
The shape of the activation function
The number of nodes in the hidden layer …
Batali suggests: the initial connection weights.
Normally, these are random – but what if they were specified by genes?

5. How to model an organism
GENOTYPE
development
PHENOTYPE
The model has genes, which are expressed as a phenotype.
The phenotype is simply the initial state of a network (before learning).

6. How to evolve organisms
Crucial aspects of evolution:
A population of organisms (with varying phenotypes)
A task which they are trying to succeed at
A measure of how fit they are at this task
A way of selecting the fittest
A way of allowing the genes of the fittest to survive
A mechanism for introducing variation into the gene pool
Various techniques to model all of this (i.e., Genetic Algorithms, Artificial Life etc.)

7. Batali’s model of evolution
Each organism (or agent) has its weights set by genes
The agents then trained on some language
The agents’ error is used to assign fitness
Only the top third of the population is kept
The top third have their weights reset to what their genes specify
Each agent “gives birth” to two new agents with approximately the same genes (i.e., genes are mutated)
Go to step 1.

8. The language task
One of the simplest languages that involves embedding is ab
aabb
aaaaaaaabbbbbbbb
*aaaaaaaaabbbbbbbb
What machinery would you need to recognise strings from this language?
Minimally – a simple counter
Can an SRN with random initial weights learn this language?

9. Performance of a trained (but non-evolved) net
Networks fail to learn to count (although some aspects of the language are learnt).

10. Evolving a better network
Batali used a population of 24 nets (initially with genes specifying random weights)
Evolved using a fitness function based on ability at after training
After 150 generations, the networks were better at learning the task
They evolved initial weight settings that made learning syntax possible

11. Evolved network performanceb sp
rec

13. Issues that remain… What is learning doing?
If language is always the same, the networks could eventually end up with the whole thing innate (and not need learning at all!)
What would happen if the networks were trained on a class of languages?
Initial weights are a different type of innateness than Elman’s. Can Batali also explain the critical period?

14. Is evolution just the same as learning?
We can think of a fitness landscape just like an error surface
What are the differences?
Does evolution do gradient descent?