@inproceedings{prange-etal-2022-linguistic,
    title = "Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling",
    author = "Prange, Jakob  and
      Schneider, Nathan  and
      Kong, Lingpeng",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.naacl-main.325/",
    doi = "10.18653/v1/2022.naacl-main.325",
    pages = "4375--4391",
    abstract = "We examine the extent to which, in principle, different syntactic and semantic graph representations can complement and improve neural language modeling. Specifically, by conditioning on a subgraph encapsulating the locally relevant sentence history, can a model make better next-word predictions than a pretrained sequential language model alone? With an ensemble setup consisting of GPT-2 and ground-truth graphs from one of 7 different formalisms, we find that the graph information indeed improves perplexity and other metrics. Moreover, this architecture provides a new way to compare different frameworks of linguistic representation. In our oracle graph setup, training and evaluating on English WSJ, semantic constituency structures prove most useful to language modeling performance{---}outpacing syntactic constituency structures as well as syntactic and semantic dependency structures."
}