@inproceedings{khalighinejad-etal-2023-approximating,
    title = "Approximating {CKY} with Transformers",
    author = "Khalighinejad, Ghazal  and
      Liu, Ollie  and
      Wiseman, Sam",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.findings-emnlp.934/",
    doi = "10.18653/v1/2023.findings-emnlp.934",
    pages = "14016--14030",
    abstract = "We investigate the ability of transformer models to approximate the CKY algorithm, using them to directly predict a sentence`s parse and thus avoid the CKY algorithm`s cubic dependence on sentence length. We find that on standard constituency parsing benchmarks this approach achieves competitive or better performance than comparable parsers that make use of CKY, while being faster. We also evaluate the viability of this approach for parsing under \textit{random} PCFGs. Here we find that performance declines as the grammar becomes more ambiguous, suggesting that the transformer is not fully capturing the CKY computation. However, we also find that incorporating additional inductive bias is helpful, and we propose a novel approach that makes use of gradients with respect to chart representations in predicting the parse, in analogy with the CKY algorithm being a subgradient of a partition function variant with respect to the chart."
}