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

4.4 Forgetting Effects
the other hand it is possible that regularity does not have a relevant impact on empirical
studies of Mandarin extraction preferences and the explanation is left to other factors.
4.4 Forgetting Effects
4.4.1 The model
As presented in chapter 3, the forgetting effect in center-embedded structures was addressed
in a connectionist study by Christiansen and Chater (1999). They trained an
SRN on right-branching and center-embedding structures and then assessed the output
node activations after seeing the sequence NNNVV. The activations showed a clear 2VP
preference consistent with empirical data from English speakers. The artificial language
that covered center-embedding abba and right-branching aabb dependency patterns is
perfectly comparable to the simple English grammar of object and subject relative
clauses used by MacDonald and Christiansen (2002). Thus, it should be possible to
replicate the effect with the SRNs trained on the English grammar for the replication
in section 4.2. In German RCs, however, no real right-branching occurs, given the embedded
RC is always attached to its head noun. Hence, in the German grammar used
in section 4.2 both ORC and SRC exhibit a center-embedding abba pattern. This fact
could result in the SRN exposed to a German grammar being more trained on verb-final
center-embedding structures than the English counterpart resulting in different predictions
for an NNNVV sequence. Supposing that the difference in SRC realization in
the corpora approximately reflects an essential word order regularity difference between
German and English, the SRN predictions will shed light on the part that experience
plays in the explanation for the forgetting effect.
I extended the study by Christiansen and Chater (1999) to gain GPE values for both
conditions on all regions after the missing verb. In order to achieve that, it was necessary
to have a grammar that simulates the forgetting effect, hence allows NNNVV sequences
to be complete. Thus, in the probability table for the drop-V2 testing corpus the column
referring to the position of V2 was deleted. In consequence the testing probabilities
were adequate to a ‘N1 N2 N3 V3 V1’ grammar with the first verb (V3) being bound
to N1 by number agreement and the second verb (V1) to N3. This is equivalent to
forgetting the prediction induced by N2. The GPE for the ungrammatical conditions was
calculated against these drop-V2 probabilities. So, if the network is making grammatical
predictions, the error values for V1 and subsequent regions should be higher in the
drop-V2 condition. On N1 the SRN would predict a verb in number agreement with
N2. Then the network would predict another verb, but the test grammar predicts the
determiner. After this point the network’s predictions should be completely confused
because the just observed sequence is inconsistent with any structural generalizations
developed during training. If the networks predictions are not too locally dependent,
the predictions should be wrong for the last word (direct object of the main clause), too.
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