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        <title>Associative Multichannel Autoencoder for Multimodal Word Representation</title>
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        <namePart type="given">Shaonan</namePart>
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        <namePart type="given">Jiajun</namePart>
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    <name type="personal">
        <namePart type="given">Chengqing</namePart>
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    <abstract>In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs. Inspired by the re-constructive and associative nature of human memory, we propose a novel associative multichannel autoencoder (AMA). Our model first learns the associations between textual and perceptual modalities, so as to predict the missing perceptual information of concepts. Then the textual and predicted perceptual representations are fused through reconstructing their original and associated embeddings. Using a gating mechanism our model assigns different weights to each modality according to the different concepts. Results on six benchmark concept similarity tests show that the proposed method significantly outperforms strong unimodal baselines and state-of-the-art multimodal models.</abstract>
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%0 Conference Proceedings
%T Associative Multichannel Autoencoder for Multimodal Word Representation
%A Wang, Shaonan
%A Zhang, Jiajun
%A Zong, Chengqing
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wang-etal-2018-associative
%X In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs. Inspired by the re-constructive and associative nature of human memory, we propose a novel associative multichannel autoencoder (AMA). Our model first learns the associations between textual and perceptual modalities, so as to predict the missing perceptual information of concepts. Then the textual and predicted perceptual representations are fused through reconstructing their original and associated embeddings. Using a gating mechanism our model assigns different weights to each modality according to the different concepts. Results on six benchmark concept similarity tests show that the proposed method significantly outperforms strong unimodal baselines and state-of-the-art multimodal models.
%R 10.18653/v1/D18-1011
%U https://aclanthology.org/D18-1011
%U https://doi.org/10.18653/v1/D18-1011
%P 115-124

Markdown (Informal)

[Associative Multichannel Autoencoder for Multimodal Word Representation](https://aclanthology.org/D18-1011) (Wang et al., EMNLP 2018)

• Associative Multichannel Autoencoder for Multimodal Word Representation (Wang et al., EMNLP 2018)

ACL

• Shaonan Wang, Jiajun Zhang, and Chengqing Zong. 2018. Associative Multichannel Autoencoder for Multimodal Word Representation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 115–124, Brussels, Belgium. Association for Computational Linguistics.