@inproceedings{jacob-etal-2018-learning,
    title = "Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences",
    author = "Jacob, Athul Paul  and
      Lin, Zhouhan  and
      Sordoni, Alessandro  and
      Bengio, Yoshua",
    editor = "Augenstein, Isabelle  and
      Cao, Kris  and
      He, He  and
      Hill, Felix  and
      Gella, Spandana  and
      Kiros, Jamie  and
      Mei, Hongyuan  and
      Misra, Dipendra",
    booktitle = "Proceedings of the Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-3020",
    doi = "10.18653/v1/W18-3020",
    pages = "154--158",
    abstract = "We propose a hierarchical model for sequential data that learns a tree on-the-fly, i.e. while reading the sequence. In the model, a recurrent network adapts its structure and reuses recurrent weights in a recursive manner. This creates adaptive skip-connections that ease the learning of long-term dependencies. The tree structure can either be inferred without supervision through reinforcement learning, or learned in a supervised manner. We provide preliminary experiments in a novel Math Expression Evaluation (MEE) task, which is created to have a hierarchical tree structure that can be used to study the effectiveness of our model. Additionally, we test our model in a well-known propositional logic and language modelling tasks. Experimental results have shown the potential of our approach.",
}