Generating novel experimental hypotheses from language models: A case
  study on cross-dative generalization
Neural network language models (LMs) have been shown to successfully capture
complex linguistic knowledge. However, their utility for understanding language
acquisition is still debated. We contribute to this debate by presenting a case
study where we use LMs as simulated learners to derive novel experimental
hypotheses to be tested with humans. We apply this paradigm to study
cross-dative generalization (CDG): productive generalization of novel verbs
across dative constructions (she pilked me the ball/she pilked the ball to me)
-- acquisition of which is known to involve a large space of contextual
features -- using LMs trained on child-directed speech. We specifically ask:
"what properties of the training exposure facilitate a novel verb's
generalization to the (unmodeled) alternate construction?" To answer this, we
systematically vary the exposure context in which a novel dative verb occurs in
terms of the properties of the theme and recipient, and then analyze the LMs'
usage of the novel verb in the unmodeled dative construction. We find LMs to
replicate known patterns of children's CDG, as a precondition to exploring
novel hypotheses. Subsequent simulations reveal a nuanced role of the features
of the novel verbs' exposure context on the LMs' CDG. We find CDG to be
facilitated when the first postverbal argument of the exposure context is
pronominal, definite, short, and conforms to the prototypical animacy
expectations of the exposure dative. These patterns are characteristic of
harmonic alignment in datives, where the argument with features ranking higher
on the discourse prominence scale tends to precede the other. This gives rise
to a novel hypothesis that CDG is facilitated insofar as the features of the
exposure context -- in particular, its first postverbal argument -- are
harmonically aligned. We conclude by proposing future experiments that can test
this hypothesis in children.
66b9c9b513f3018a121f1804
Kanishka Misra
extracted_pending
2024-08-12T08:37:10.324Z
60289f024d503386b8e75642
/avatars/ddcb731d0abcf02933d7462b3da6f5fe.svg
Kanishka Misra
user
kanishka
66b9c9b513f3018a121f1805
Najoung Kim
extracted_pending
2024-08-12T08:37:10.324Z
65947c18a2607099287e23fd
/avatars/270e234d55b15f3796960db0a66a6d51.svg
Najoung Kim
user
najoung
Generating novel experimental hypotheses from language models: A case
  study on cross-dative generalization
Neural network language models (LMs) have been shown to successfully capture
complex linguistic knowledge. However, their utility for understanding language
acquisition is still debated. We contribute to this debate by presenting a case
study where we use LMs as simulated learners to derive novel experimental
hypotheses to be tested with humans. We apply this paradigm to study
cross-dative generalization (CDG): productive generalization of novel verbs
across dative constructions (she pilked me the ball/she pilked the ball to me)
-- acquisition of which is known to involve a large space of contextual
features -- using LMs trained on child-directed speech. We specifically ask:
"what properties of the training exposure facilitate a novel verb's
generalization to the (unmodeled) alternate construction?" To answer this, we
systematically vary the exposure context in which a novel dative verb occurs in
terms of the properties of the theme and recipient, and then analyze the LMs'
usage of the novel verb in the unmodeled dative construction. We find LMs to
replicate known patterns of children's CDG, as a precondition to exploring
novel hypotheses. Subsequent simulations reveal a nuanced role of the features
of the novel verbs' exposure context on the LMs' CDG. We find CDG to be
facilitated when the first postverbal argument of the exposure context is
pronominal, definite, short, and conforms to the prototypical animacy
expectations of the exposure dative. These patterns are characteristic of
harmonic alignment in datives, where the argument with features ranking higher
on the discourse prominence scale tends to precede the other. This gives rise
to a novel hypothesis that CDG is facilitated insofar as the features of the
exposure context -- in particular, its first postverbal argument -- are
harmonically aligned. We conclude by proposing future experiments that can test
this hypothesis in children.
Neural network language models simulate children's acquisition of cross-dative generalization, revealing that the harmonically aligned features of the exposure context facilitate generalization to unmodeled constructions.
neural network language models cross-dative generalization child-directed speech harmonic alignment discourse prominence