@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.",
}
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%0 Conference Proceedings
%T Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences
%A Jacob, Athul Paul
%A Lin, Zhouhan
%A Sordoni, Alessandro
%A Bengio, Yoshua
%Y Augenstein, Isabelle
%Y Cao, Kris
%Y He, He
%Y Hill, Felix
%Y Gella, Spandana
%Y Kiros, Jamie
%Y Mei, Hongyuan
%Y Misra, Dipendra
%S Proceedings of the Third Workshop on Representation Learning for NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F jacob-etal-2018-learning
%X 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.
%R 10.18653/v1/W18-3020
%U https://aclanthology.org/W18-3020
%U https://doi.org/10.18653/v1/W18-3020
%P 154-158
Markdown (Informal)
[Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences](https://aclanthology.org/W18-3020) (Jacob et al., RepL4NLP 2018)
ACL