@inproceedings{howitt-etal-2024-generalizations,
    title = "Generalizations across filler-gap dependencies in neural language models",
    author = "Howitt, Katherine  and
      Nair, Sathvik  and
      Dods, Allison  and
      Hopkins, Robert Melvin",
    editor = "Barak, Libby  and
      Alikhani, Malihe",
    booktitle = "Proceedings of the 28th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2024",
    address = "Miami, FL, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.conll-1.21/",
    doi = "10.18653/v1/2024.conll-1.21",
    pages = "269--279",
    abstract = "Humans develop their grammars by making structural generalizations from finite input. We ask how filler-gap dependencies (FGDs), which share a structural generalization despite diverse surface forms, might arise from the input. We explicitly control the input to a neural language model (NLM) to uncover whether the model posits a shared representation for FGDs. We show that while NLMs do have success differentiating grammatical from ungrammatical FGDs, they rely on superficial properties of the input, rather than on a shared generalization. Our work highlights the need for specific linguistic inductive biases to model language acquisition."
}