@inproceedings{casas-etal-2020-syntax,
    title = "Syntax-driven Iterative Expansion Language Models for Controllable Text Generation",
    author = "Casas, Noe  and
      Fonollosa, Jos{\'e} A. R.  and
      Costa-juss{\`a}, Marta R.",
    editor = "Agrawal, Priyanka  and
      Kozareva, Zornitsa  and
      Kreutzer, Julia  and
      Lampouras, Gerasimos  and
      Martins, Andr{\'e}  and
      Ravi, Sujith  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.spnlp-1.1",
    doi = "10.18653/v1/2020.spnlp-1.1",
    pages = "1--10",
    abstract = "The dominant language modeling paradigm handles text as a sequence of discrete tokens. While that approach can capture the latent structure of the text, it is inherently constrained to sequential dynamics for text generation. We propose a new paradigm for introducing a syntactic inductive bias into neural text generation, where the dependency parse tree is used to drive the Transformer model to generate sentences iteratively. Our experiments show that this paradigm is effective at text generation, with quality between LSTMs and Transformers, and comparable diversity, requiring less than half their decoding steps, and its generation process allows direct control over the syntactic constructions of the generated text, enabling the induction of stylistic variations.",
}