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

Chapter 3 Connectionist Modelling of Language Comprehension
this point the SRN highly predicts a determiner after seeing an ORC. This points to a
locally consistent interpretation of the embedded ‘. . . the N Vtrans’ sequence as a main
clause prefix continuing with an NP. This misinterpretation is reduced in later training
epochs. An additional and very stable false prediction on the main verb is the EOS
after embedded SRCs and ORCs. Concerning the SRC this is consistent with a locally
coherent interpretation of the SRC sequence ‘. . . Vtrans the N’ as part of a main clause.
In the ORC on the other hand, the EOS prediction after the ‘. . . the N Vtrans’ sequence
is only locally consistent when interpreting the transitive verb as intransitive. This
seems on first sight to be consistent with the assumption of Wells et al. (2009) that
the SRN has to learn the trans/intrans difference. But surprisingly the wrong EOS
prediction increases with further training, indicating that the network does not recognize
the transitivity of the embedded verb. Summarizing the analysis of Konieczny and Ruh,
what causes the effects on embedded and main verb is a) the interpretation of that as
a verb, b) the prediction of the sentence to end after an embedded RC due to local
coherence, and c) the failure to classify transitive and intransitive verbs. Konieczny and
Ruh suggest to abandon verbs that can be both transitive and intransitive from the
lexicon to separate the two classes more clearly. Furthermore the grammar should allow
the use of pronominal NPs to move the classification of that nearer to nouns than verbs.
Concerning the German RC simulations the explanation of the effects is quite simple.
German SRCs and ORCs differ only in the serial order of the the relative pronoun and the
determiner of the embedded NP. Consequently the SRC contains in this region a NOM-
ACC sequence whereas the SRC contains an ACC-NOM sequence. The embedded verb
always agrees with the nominative (der). This produces a locally consistent structure of
‘detnom N V’ in the ORC but not in the SRC. Following Konieczny and Ruh this local
consistency effect produces the correct predictions for the embedded verb in the ORC,
which is the reason for the lower error. In the SRC the verb is bound to the relative
pronoun, which shares the number with the matrix subject. The SRN’s verb predictions,
however, seem to be more influenced by the number of the intervening object than by
the distant dependency.
3.3.4 Summary
In using SRNs MacDonald and Christiansen (2002) take advantage of a simple mechanism
that, however, without architectural predesign makes excellent predictions concerning
the functional relation between exposure to certain structures and processing skill.
The model’s behavior is interpretable in terms of memory and decay, but due to its
temporal loop and learning mechanism it sensitive to context and experience. The King
and Just data was well fitted, especially for individual differences. The model results
in combination with the study by Wells et al. (2009) provide a comprehensive skillthrough-experience
account that includes individual and language-specific differences.
Konieczny and Ruh (2003) and others question the model’s validity, partly because a
detailed analysis shows that learned constraints are of local nature and not comparable
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