<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pavez-etal-2018-working">
    <titleInfo>
        <title>Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Juan</namePart>
        <namePart type="family">Pavez</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Héctor</namePart>
        <namePart type="family">Allende</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Héctor</namePart>
        <namePart type="family">Allende-Cid</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that could allow, for instance, relational reasoning. Relation Networks (RNs), on the other hand, have shown outstanding results in relational reasoning tasks. Unfortunately, their computational cost grows quadratically with the number of memories, something prohibitive for larger problems. To solve these issues, we introduce the Working Memory Network, a MemNN architecture with a novel working memory storage and reasoning module. Our model retains the relational reasoning abilities of the RN while reducing its computational complexity from quadratic to linear. We tested our model on the text QA dataset bAbI and the visual QA dataset NLVR. In the jointly trained bAbI-10k, we set a new state-of-the-art, achieving a mean error of less than 0.5%. Moreover, a simple ensemble of two of our models solves all 20 tasks in the joint version of the benchmark.</abstract>
    <identifier type="citekey">pavez-etal-2018-working</identifier>
    <identifier type="doi">10.18653/v1/P18-1092</identifier>
    <location>
        <url>https://aclanthology.org/P18-1092</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>1000</start>
            <end>1009</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module
%A Pavez, Juan
%A Allende, Héctor
%A Allende-Cid, Héctor
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F pavez-etal-2018-working
%X During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that could allow, for instance, relational reasoning. Relation Networks (RNs), on the other hand, have shown outstanding results in relational reasoning tasks. Unfortunately, their computational cost grows quadratically with the number of memories, something prohibitive for larger problems. To solve these issues, we introduce the Working Memory Network, a MemNN architecture with a novel working memory storage and reasoning module. Our model retains the relational reasoning abilities of the RN while reducing its computational complexity from quadratic to linear. We tested our model on the text QA dataset bAbI and the visual QA dataset NLVR. In the jointly trained bAbI-10k, we set a new state-of-the-art, achieving a mean error of less than 0.5%. Moreover, a simple ensemble of two of our models solves all 20 tasks in the joint version of the benchmark.
%R 10.18653/v1/P18-1092
%U https://aclanthology.org/P18-1092
%U https://doi.org/10.18653/v1/P18-1092
%P 1000-1009

Markdown (Informal)

[Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module](https://aclanthology.org/P18-1092) (Pavez et al., ACL 2018)

• Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module (Pavez et al., ACL 2018)

ACL

• Juan Pavez, Héctor Allende, and Héctor Allende-Cid. 2018. Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1000–1009, Melbourne, Australia. Association for Computational Linguistics.