OT learning 2.4. Evaluation


In an OptimalityTheoretic model of grammar, evaluation refers to the determination of the winning candidate on the basis of the constraint ranking.
In an ordinal OT model of grammar, repeated evaluations will yield the same winner again and again. We can simulate this behaviour with our NOCODA example. In the editor, you can choose Evaluate (zero noise) or use its keyboard shortcut Command0 (= Commandzero). Repeated evaluations (keep Command0 pressed) will always yield the following grammar:

 ranking value  disharmony  plasticity 

NOCODA  100.000  100.000  1.000 

PARSE  90.000  90.000  1.000 
In a stochastic OT model of grammar, repeated evaluations will yield different disharmonies each time. To see this, choose Evaluate (noise 2.0) or use its keyboard shortcut Command2. Repeated evaluations will yield grammars like the following:

 ranking value  disharmony  plasticity 

NOCODA  100.000  100.427  1.000 

PARSE  90.000  87.502  1.000 
and

 ranking value  disharmony  plasticity 

NOCODA  100.000  101.041  1.000 

PARSE  90.000  90.930  1.000 
and

 ranking value  disharmony  plasticity 

NOCODA  100.000  96.398  1.000 

PARSE  90.000  89.482  1.000 
The disharmonies vary around the ranking values, according to a Gaussian distribution with a standard deviation of 2.0. The winner will still be [pa] in almost all cases, because the probability of bridging the gap between the two ranking values is very low, namely 0.02 per cent according to Boersma (1998), page 332.
With a noise much higher than 2.0, the chances of PARSE outranking NOCODA will rise. To see this, choose Evaluate... and supply 5.0 for the noise. Typical outcomes are:

 ranking value  disharmony  plasticity 

NOCODA  100.000  92.634  1.000 

PARSE  90.000  86.931  1.000 
and

 ranking value  disharmony  plasticity 

NOCODA  100.000  101.162  1.000 

PARSE  90.000  85.311  1.000 
and

 ranking value  disharmony  plasticity 

PARSE  90.000  99.778  1.000 

NOCODA  100.000  98.711  1.000 
In the last case, the order of the constraints has been reversed. You will see that [pat] has become the winning candidate:
However, in the remaining part of this tutorial, we will stick with a noise with a standard deviation of 2.0. This specific number ensures that we can model fairly rigid rankings by giving the constraints a ranking difference of 10, a nice round number. Also, the learning algorithm will separate many constraints in such a way that the differences between their ranking values are in the vicinity of 10.
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