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    <titleInfo>
        <title>Like a Baby: Visually Situated Neural Language Acquisition</title>
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        <namePart type="given">Alexander</namePart>
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    <name type="personal">
        <namePart type="given">Ankur</namePart>
        <namePart type="family">Mali</namePart>
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        <namePart type="family">Kelly</namePart>
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    <name type="personal">
        <namePart type="given">David</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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    <abstract>We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling framework yields a 3.5% improvement. The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, Delta-RNN, as well as those that use BERT embeddings). Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment. This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.</abstract>
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%0 Conference Proceedings
%T Like a Baby: Visually Situated Neural Language Acquisition
%A Ororbia, Alexander
%A Mali, Ankur
%A Kelly, Matthew
%A Reitter, David
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluí s.
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F ororbia-etal-2019-like
%X We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling framework yields a 3.5% improvement. The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, Delta-RNN, as well as those that use BERT embeddings). Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment. This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.
%R 10.18653/v1/P19-1506
%U https://aclanthology.org/P19-1506/
%U https://doi.org/10.18653/v1/P19-1506
%P 5127-5136

Markdown (Informal)

[Like a Baby: Visually Situated Neural Language Acquisition](https://aclanthology.org/P19-1506/) (Ororbia et al., ACL 2019)

• Like a Baby: Visually Situated Neural Language Acquisition (Ororbia et al., ACL 2019)

ACL

• Alexander Ororbia, Ankur Mali, Matthew Kelly, and David Reitter. 2019. Like a Baby: Visually Situated Neural Language Acquisition. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5127–5136, Florence, Italy. Association for Computational Linguistics.