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        <title>Limitations in learning an interpreted language with recurrent models</title>
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    <name type="personal">
        <namePart type="given">Denis</namePart>
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            <title>Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP</title>
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        <name type="personal">
            <namePart type="given">Tal</namePart>
            <namePart type="family">Linzen</namePart>
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        <name type="personal">
            <namePart type="given">Grzegorz</namePart>
            <namePart type="family">Chrupała</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Afra</namePart>
            <namePart type="family">Alishahi</namePart>
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    <abstract>In this submission I report work in progress on learning simplified interpreted languages by means of recurrent models. The data is constructed to reflect core properties of natural language as modeled in formal syntax and semantics. Preliminary results suggest that LSTM networks do generalise to compositional interpretation, albeit only in the most favorable learning setting.</abstract>
    <identifier type="citekey">paperno-2018-limitations</identifier>
    <identifier type="doi">10.18653/v1/W18-5456</identifier>
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        <url>https://aclanthology.org/W18-5456</url>
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%0 Conference Proceedings
%T Limitations in learning an interpreted language with recurrent models
%A Paperno, Denis
%Y Linzen, Tal
%Y Chrupała, Grzegorz
%Y Alishahi, Afra
%S Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F paperno-2018-limitations
%X In this submission I report work in progress on learning simplified interpreted languages by means of recurrent models. The data is constructed to reflect core properties of natural language as modeled in formal syntax and semantics. Preliminary results suggest that LSTM networks do generalise to compositional interpretation, albeit only in the most favorable learning setting.
%R 10.18653/v1/W18-5456
%U https://aclanthology.org/W18-5456
%U https://doi.org/10.18653/v1/W18-5456
%P 384-386

Markdown (Informal)

[Limitations in learning an interpreted language with recurrent models](https://aclanthology.org/W18-5456) (Paperno, EMNLP 2018)

• Limitations in learning an interpreted language with recurrent models (Paperno, EMNLP 2018)

ACL

• Denis Paperno. 2018. Limitations in learning an interpreted language with recurrent models. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 384–386, Brussels, Belgium. Association for Computational Linguistics.