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    <titleInfo>
        <title>Decoding Language Spatial Relations to 2D Spatial Arrangements</title>
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    <name type="personal">
        <namePart type="given">Gorjan</namePart>
        <namePart type="family">Radevski</namePart>
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        <namePart type="given">Guillem</namePart>
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    <name type="personal">
        <namePart type="given">Marie-Francine</namePart>
        <namePart type="family">Moens</namePart>
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    <name type="personal">
        <namePart type="given">Tinne</namePart>
        <namePart type="family">Tuytelaars</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2020</title>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>We address the problem of multimodal spatial understanding by decoding a set of language-expressed spatial relations to a set of 2D spatial arrangements in a multi-object and multi-relationship setting. We frame the task as arranging a scene of clip-arts given a textual description. We propose a simple and effective model architecture Spatial-Reasoning Bert (SR-Bert), trained to decode text to 2D spatial arrangements in a non-autoregressive manner. SR-Bert can decode both explicit and implicit language to 2D spatial arrangements, generalizes to out-of-sample data to a reasonable extent and can generate complete abstract scenes if paired with a clip-arts predictor. Finally, we qualitatively evaluate our method with a user study, validating that our generated spatial arrangements align with human expectation.</abstract>
    <identifier type="citekey">radevski-etal-2020-decoding</identifier>
    <identifier type="doi">10.18653/v1/2020.findings-emnlp.408</identifier>
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        <date>2020-11</date>
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            <start>4549</start>
            <end>4560</end>
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%0 Conference Proceedings
%T Decoding Language Spatial Relations to 2D Spatial Arrangements
%A Radevski, Gorjan
%A Collell, Guillem
%A Moens, Marie-Francine
%A Tuytelaars, Tinne
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F radevski-etal-2020-decoding
%X We address the problem of multimodal spatial understanding by decoding a set of language-expressed spatial relations to a set of 2D spatial arrangements in a multi-object and multi-relationship setting. We frame the task as arranging a scene of clip-arts given a textual description. We propose a simple and effective model architecture Spatial-Reasoning Bert (SR-Bert), trained to decode text to 2D spatial arrangements in a non-autoregressive manner. SR-Bert can decode both explicit and implicit language to 2D spatial arrangements, generalizes to out-of-sample data to a reasonable extent and can generate complete abstract scenes if paired with a clip-arts predictor. Finally, we qualitatively evaluate our method with a user study, validating that our generated spatial arrangements align with human expectation.
%R 10.18653/v1/2020.findings-emnlp.408
%U https://aclanthology.org/2020.findings-emnlp.408
%U https://doi.org/10.18653/v1/2020.findings-emnlp.408
%P 4549-4560

Markdown (Informal)

[Decoding Language Spatial Relations to 2D Spatial Arrangements](https://aclanthology.org/2020.findings-emnlp.408) (Radevski et al., Findings 2020)

• Decoding Language Spatial Relations to 2D Spatial Arrangements (Radevski et al., Findings 2020)

ACL

• Gorjan Radevski, Guillem Collell, Marie-Francine Moens, and Tinne Tuytelaars. 2020. Decoding Language Spatial Relations to 2D Spatial Arrangements. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4549–4560, Online. Association for Computational Linguistics.