Connectionist Modeling of Experience-based Effects in Sentence ...
Connectionist Modeling of Experience-based Effects in Sentence ...
Connectionist Modeling of Experience-based Effects in Sentence ...
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4.3 RC Extraction <strong>in</strong> Mandar<strong>in</strong><br />
The GPE score measured on a certa<strong>in</strong> word only tells us how the predictions <strong>based</strong><br />
on previous words fit the probabilistic grammar. It does not <strong>in</strong>clude any effect <strong>of</strong> the<br />
current word itself.<br />
GPE<br />
0.0 0.2 0.4 0.6 0.8 1.0<br />
Mandar<strong>in</strong> SRC<br />
N1 de N2 V2 N3<br />
Region<br />
epoch 1<br />
epoch 2<br />
epoch 3<br />
GPE<br />
0.0 0.2 0.4 0.6 0.8 1.0<br />
Mandar<strong>in</strong> ORC<br />
V1 de N2 V2 N3<br />
Region<br />
Figure 4.4: Simulation 1: Mandar<strong>in</strong> ORC regularity.<br />
epoch 1<br />
epoch 2<br />
epoch 3<br />
See figure 4.4 for GPE scores <strong>of</strong> SRCs and ORCs by tra<strong>in</strong><strong>in</strong>g epochs. For means<br />
and standard errors see table A.1 <strong>in</strong> the appendix. Collaps<strong>in</strong>g over all regions and<br />
epochs there was a significant advantage for object relatives. The difference shrank with<br />
<strong>in</strong>creas<strong>in</strong>g epochs. For the ORC there was significant improvement on the ma<strong>in</strong> verb<br />
over the three epochs. The SRC improved on the ma<strong>in</strong> verb and the relativizer. On<br />
the first region (N1/V1) there was a marg<strong>in</strong>al advantage for the ORC <strong>in</strong> the first epoch.<br />
The second region (de) showed a significant object advantage <strong>in</strong> all epochs. There was<br />
also an object advantage on region 4 (V2), which, however, disappeared after the second<br />
epoch due to SRC improvement. Region three and five did not show any effect.<br />
The results <strong>of</strong> experiment 1 showed the predicted frequency × regularity <strong>in</strong>teraction.<br />
In contrast to the English results by MC02 the regularity effect is seen <strong>in</strong> object relatives<br />
<strong>in</strong> Mandar<strong>in</strong>. The effect, however, is is not located on the embedded RC but ma<strong>in</strong>ly on<br />
the relativizer. It seems as the predictions for position 4 (here the relativizer) are easier<br />
for the ORC because <strong>of</strong> the familiarity with the sequence ‘N V . . . ’ where the relativizer<br />
should have a quite low cont<strong>in</strong>uation probability due to the small RC frequency <strong>in</strong> the<br />
corpus. On the other hand, the SRC sequence ‘V N’ is very rarely occurr<strong>in</strong>g at the<br />
sentence beg<strong>in</strong>n<strong>in</strong>g, mak<strong>in</strong>g more tra<strong>in</strong><strong>in</strong>g necessary to learn the correct predictions.<br />
Over tra<strong>in</strong><strong>in</strong>g the network has to learn to assign a high activation to the relativizer after<br />
an ‘V N’ and to exclude almost all other words as a cont<strong>in</strong>uation.<br />
Experiment 1 superficially confirms the ORC regularity hypothesis. However, as the<br />
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