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    <titleInfo>
        <title>Representation Learning for Grounded Spatial Reasoning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="family">Janner</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Karthik</namePart>
        <namePart type="family">Narasimhan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    </name>
    <name type="personal">
        <namePart type="given">Regina</namePart>
        <namePart type="family">Barzilay</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <abstract>The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.</abstract>
    <identifier type="citekey">janner-etal-2018-representation</identifier>
    <identifier type="doi">10.1162/tacl_a_00004</identifier>
    <location>
        <url>https://aclanthology.org/Q18-1004</url>
    </location>
    <part>
        <date>2018</date>
        <detail type="volume"><number>6</number></detail>
        <extent unit="page">
            <start>49</start>
            <end>61</end>
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%0 Journal Article
%T Representation Learning for Grounded Spatial Reasoning
%A Janner, Michael
%A Narasimhan, Karthik
%A Barzilay, Regina
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F janner-etal-2018-representation
%X The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.
%R 10.1162/tacl_a_00004
%U https://aclanthology.org/Q18-1004
%U https://doi.org/10.1162/tacl_a_00004
%P 49-61

Markdown (Informal)

[Representation Learning for Grounded Spatial Reasoning](https://aclanthology.org/Q18-1004) (Janner et al., TACL 2018)

• Representation Learning for Grounded Spatial Reasoning (Janner et al., TACL 2018)

ACL

• Michael Janner, Karthik Narasimhan, and Regina Barzilay. 2018. Representation Learning for Grounded Spatial Reasoning. Transactions of the Association for Computational Linguistics, 6:49–61.