A LAD for OT

1
? A LAD for OT

• Markedness in acquisition
• Hypothesis Universal markedness principles are
  genetically encoded, learning is search among
  UG-permitted grammars.
• Question Is this even conceivable?
• Collaborators
• Melanie Soderstrom Donald Mathis
• Oren Schwartz

2
CVNet Architecture

• /C1 C2/ ? C1 V C2

/ C1 C2 /
COns1 V CCod2
3
Connection substructure
s2
i
2
1

• Network weight
• Network input ? W?? a?

4
PARSE (MAX)
5
NOCODA
6
CVNet Dynamics

• Boltzmann machine/Harmony network
• Learning modification of Boltzmann machine
  algorithm to new architecture
• Algorithm minimizes distance to correct output
  distribution
• Stochastic activation algorithm during
  processing, higher Harmony ? more probable
• Final state local maximum guaranteed if slow
  enough, global maximum probable

7
Learning Behavior

• CVNet can only learn grammars consisting exactly
  of the CV-theory constraints Con with
  differential strengths
• No guarantee of strict domination
• A simplified system can be solved analytically
• Learning algorithm turns out to
• Dsi(?) e violations of constrainti P?

8
To be encoded

• How many different kinds of units are there?
• What information is necessary (from the source
  units point of view) to identify the location of
  a target unit, and the strength of the connection
  with it?
• How are constraints initially specified?
• How are they maintained through the learning
  process?

9
Unit types

• Input units C V
• Output units COns V CCod
• Correspondence units C V
• 7 distinct unit types
• Each represented in a distinct sub-region of the
  abstract genome
• Help ourselves to implicit machinery to spell
  out these sub-regions as distinct cell types,
  located in grid as illustrated

10
Connectivity geometry

• Assume 3-d grid geometry

11
Constraint PARSE

• Input units grow south and connect
• Output units grow east and connect
• Correspondence units grow north west and
  connect with input output units.

12
Constraint ONSET

• Short connections grow north from V units and
  connect to COns unit

13
Direction of projection growth

• Topographic organizations widely attested
  throughout neural structures
• Activity-dependent growth a possible alternative
• Orientation information (axes)
• Chemical gradients during development
• Cell age a possible alternative

14
Projection parameters

• Direction
• Extent
• Local
• Non-local
• Target unit type
• Strength of connections encoded separately

15
Connectivity Genome

• Contributions from ONSET and PARSE

• Key

16
ONSET
COns segment S S VO

• VO segment N S COns

17
Learning Behavior

• Simplified system can be solved analytically
• Learning algorithm turns out to
• Dsi(?) e violations of constrainti P?

18
Conclusion

• Markedness principles, as Harmony in OT
• Provides basis for analysis of
• alternations
• typology
• ? Provides the basis for theory of processing in
  neural networks
• ? Provides the content of UG in CV-LAD