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        <title>The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning</title>
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        <namePart type="given">Yuchen</namePart>
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    <name type="personal">
        <namePart type="given">Arianna</namePart>
        <namePart type="family">Bisazza</namePart>
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    <name type="personal">
        <namePart type="given">Tessa</namePart>
        <namePart type="family">Verhoef</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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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">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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                <placeTerm type="text">Online and Punta Cana, Dominican Republic</placeTerm>
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    <abstract>Natural languages display a trade-off among different strategies to convey syntactic structure, such as word order or inflection. This trade-off, however, has not appeared in recent simulations of iterated language learning with neural network agents (Chaabouni et al., 2019b). We re-evaluate this result in light of three factors that play an important role in comparable experiments from the Language Evolution field: (i) speaker bias towards efficient messaging, (ii) non systematic input languages, and (iii) learning bottleneck. Our simulations show that neural agents mainly strive to maintain the utterance type distribution observed during learning, instead of developing a more efficient or systematic language.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.794</identifier>
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        <date>2021-11</date>
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            <start>10121</start>
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%0 Conference Proceedings
%T The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning
%A Lian, Yuchen
%A Bisazza, Arianna
%A Verhoef, Tessa
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F lian-etal-2021-effect
%X Natural languages display a trade-off among different strategies to convey syntactic structure, such as word order or inflection. This trade-off, however, has not appeared in recent simulations of iterated language learning with neural network agents (Chaabouni et al., 2019b). We re-evaluate this result in light of three factors that play an important role in comparable experiments from the Language Evolution field: (i) speaker bias towards efficient messaging, (ii) non systematic input languages, and (iii) learning bottleneck. Our simulations show that neural agents mainly strive to maintain the utterance type distribution observed during learning, instead of developing a more efficient or systematic language.
%R 10.18653/v1/2021.emnlp-main.794
%U https://aclanthology.org/2021.emnlp-main.794
%U https://doi.org/10.18653/v1/2021.emnlp-main.794
%P 10121-10129

Markdown (Informal)

[The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning](https://aclanthology.org/2021.emnlp-main.794) (Lian et al., EMNLP 2021)

• The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning (Lian et al., EMNLP 2021)

ACL

• Yuchen Lian, Arianna Bisazza, and Tessa Verhoef. 2021. The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10121–10129, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.