CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Associative Multichannel Autoencoder for Multimodal Word Representation
Wang, Shaonan; Zhang, Jiajun; Zong, Chengqing
2018
Conference NameEMNLP
Conference Date2018-11
Conference PlaceBrussels, Belgium
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

Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23198
Collection模式识别国家重点实验室_自然语言处理
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Shaonan,Zhang, Jiajun,Zong, Chengqing. Associative Multichannel Autoencoder for Multimodal Word Representation[C],2018.
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