title: Generating novel experimental hypotheses from language models: A case
    study on cross-dative generalization
summary: 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.
paper_authors._id: 66b9c9b513f3018a121f1804
paper_authors.name: Kanishka Misra
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-08-12T08:37:10.324Z
paper_authors._id: 60289f024d503386b8e75642
paper_authors.avatarUrl: /avatars/ddcb731d0abcf02933d7462b3da6f5fe.svg
paper_authors.fullname: Kanishka Misra
paper_authors.type: user
paper_authors.user: kanishka
paper_authors._id: 66b9c9b513f3018a121f1805
paper_authors.name: Najoung Kim
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-08-12T08:37:10.324Z
paper_authors._id: 65947c18a2607099287e23fd
paper_authors.avatarUrl: /avatars/270e234d55b15f3796960db0a66a6d51.svg
paper_authors.fullname: Najoung Kim
paper_authors.type: user
paper_authors.user: najoung
paper_title: Generating novel experimental hypotheses from language models: A case
    study on cross-dative generalization
paper_summary: 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.
paper_ai_keywords: neural network language models (LMs) simulated learners cross-dative generalization (CDG) parallel verbs dative constructions child-directed speech theme recipient discourse prominence scale harmonic alignment