@inproceedings{maillard-clark-2018-latent,
    title = "Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing",
    author = "Maillard, Jean  and
      Clark, Stephen",
    editor = "Dinu, Georgiana  and
      Ballesteros, Miguel  and
      Sil, Avirup  and
      Bowman, Sam  and
      Hamza, Wael  and
      Sogaard, Anders  and
      Naseem, Tahira  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-2903/",
    doi = "10.18653/v1/W18-2903",
    pages = "13--18",
    abstract = "Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model."
}