Connectionist Modeling of Experience-based Effects in Sentence ...

4.4 Forgetting Effects
GPE
0.0 0.2 0.4 0.6 0.8 1.0
English with commas (epoch 2)
V3 V2 V1 post-V1
Region
drop-V2
no-drop
GPE
0.0 0.2 0.4 0.6 0.8 1.0
English with commas (epoch 3)
V3 V2 V1 post-V1
Region
drop-V2
no-drop
Figure 4.7: Simulation 3b: English doubly-embedded ORC with added commas. The
graphic shows the GPE value on the three verbs and the subsequent region of the
grammatical (no-drop) and ungrammatical (drop-V2) condition. The left panel shows
GPE after two epochs of training, the right panel shows GPE after 3 epochs.
4.4.3 Simulation 4: German
Model Parameters
Simulation 4a tested German center-embedding with commas. The already trained networks
from section 4.2 were used. For simulation 4b training corpora created from a
German grammar without commas were used. The testing corpora were built analogically
to simulation 3.
4a: German with Commas
The German grammar exhibits a regularity for verb-finality in RCs. This is different
from the English grammar and should enable the SRN to distinguish 2VP and 3VP
embedding better than in English. As seen in the English simulation commas have
a facilitating effect although, however, the drop-VP preference returned after further
training. In German, commas could have an even greater facilitating effect. The reason
for that is that the counting-recursion pattern aabb is not only applicable in the ORC
as in English but also in the SRC because both are center-embedding in German. As
example (27) illustrates both SRC and ORC contain the exact same pattern of nouns,
verbs, and commas. In conclusion the SRN trained on the German corpus should be
very skilled on center-embeding recursion and the comma counting-recursion and hence
will have much lower error rates for the grammatical condition.
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