@inproceedings{cases-etal-2019-recursive,
title = "Recursive Routing Networks: Learning to Compose Modules for Language Understanding",
author = "Cases, Ignacio and
Rosenbaum, Clemens and
Riemer, Matthew and
Geiger, Atticus and
Klinger, Tim and
Tamkin, Alex and
Li, Olivia and
Agarwal, Sandhini and
Greene, Joshua D. and
Jurafsky, Dan and
Potts, Christopher and
Karttunen, Lauri",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1365",
doi = "10.18653/v1/N19-1365",
pages = "3631--3648",
abstract = "We introduce Recursive Routing Networks (RRNs), which are modular, adaptable models that learn effectively in diverse environments. RRNs consist of a set of functions, typically organized into a grid, and a meta-learner decision-making component called the router. The model jointly optimizes the parameters of the functions and the meta-learner{'}s policy for routing inputs through those functions. RRNs can be incorporated into existing architectures in a number of ways; we explore adding them to word representation layers, recurrent network hidden layers, and classifier layers. Our evaluation task is natural language inference (NLI). Using the MultiNLI corpus, we show that an RRN{'}s routing decisions reflect the high-level genre structure of that corpus. To show that RRNs can learn to specialize to more fine-grained semantic distinctions, we introduce a new corpus of NLI examples involving implicative predicates, and show that the model components become fine-tuned to the inferential signatures that are characteristic of these predicates.",
}
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<abstract>We introduce Recursive Routing Networks (RRNs), which are modular, adaptable models that learn effectively in diverse environments. RRNs consist of a set of functions, typically organized into a grid, and a meta-learner decision-making component called the router. The model jointly optimizes the parameters of the functions and the meta-learner’s policy for routing inputs through those functions. RRNs can be incorporated into existing architectures in a number of ways; we explore adding them to word representation layers, recurrent network hidden layers, and classifier layers. Our evaluation task is natural language inference (NLI). Using the MultiNLI corpus, we show that an RRN’s routing decisions reflect the high-level genre structure of that corpus. To show that RRNs can learn to specialize to more fine-grained semantic distinctions, we introduce a new corpus of NLI examples involving implicative predicates, and show that the model components become fine-tuned to the inferential signatures that are characteristic of these predicates.</abstract>
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%0 Conference Proceedings
%T Recursive Routing Networks: Learning to Compose Modules for Language Understanding
%A Cases, Ignacio
%A Rosenbaum, Clemens
%A Riemer, Matthew
%A Geiger, Atticus
%A Klinger, Tim
%A Tamkin, Alex
%A Li, Olivia
%A Agarwal, Sandhini
%A Greene, Joshua D.
%A Jurafsky, Dan
%A Potts, Christopher
%A Karttunen, Lauri
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F cases-etal-2019-recursive
%X We introduce Recursive Routing Networks (RRNs), which are modular, adaptable models that learn effectively in diverse environments. RRNs consist of a set of functions, typically organized into a grid, and a meta-learner decision-making component called the router. The model jointly optimizes the parameters of the functions and the meta-learner’s policy for routing inputs through those functions. RRNs can be incorporated into existing architectures in a number of ways; we explore adding them to word representation layers, recurrent network hidden layers, and classifier layers. Our evaluation task is natural language inference (NLI). Using the MultiNLI corpus, we show that an RRN’s routing decisions reflect the high-level genre structure of that corpus. To show that RRNs can learn to specialize to more fine-grained semantic distinctions, we introduce a new corpus of NLI examples involving implicative predicates, and show that the model components become fine-tuned to the inferential signatures that are characteristic of these predicates.
%R 10.18653/v1/N19-1365
%U https://aclanthology.org/N19-1365
%U https://doi.org/10.18653/v1/N19-1365
%P 3631-3648
Markdown (Informal)
[Recursive Routing Networks: Learning to Compose Modules for Language Understanding](https://aclanthology.org/N19-1365) (Cases et al., NAACL 2019)
ACL
- Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua D. Greene, Dan Jurafsky, Christopher Potts, and Lauri Karttunen. 2019. Recursive Routing Networks: Learning to Compose Modules for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3631–3648, Minneapolis, Minnesota. Association for Computational Linguistics.