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Multimodal Machine Translation

Jiatong Liu
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In order to explore how the multimodal neural network machine translation model based on deep semantic interaction designed in this paper can improve the translation results.  ...  In summary, this article has the following innovations: i) Aiming at the existing problem of lack of semantic interaction in multimodal neural network machine translation, the visual-text semantic interaction  ...  We plan to study integrating the convolutional neural network used to extract image features into the translation model and train it during the translation process.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3115135">doi:10.1109/access.2021.3115135</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d2anaeg3qnarpfmlc4eap2urrm">fatcat:d2anaeg3qnarpfmlc4eap2urrm</a> </span>
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Special issue on semantic deep learning

Dagmar Gromann, Luis Espinosa Anke, Thierry Declerck, Pascal Hitzler, Krzysztof Janowicz
<span title="2019-08-30">2019</span> <i title="IOS Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pcapks3huberdozbvfqowysuly" style="color: black;">Semantic Web Journal</a> </i> &nbsp;
This editorial introduces the Semantic Web Journal special issue on Semantic Deep Learning, which brings together Semantic Web and deep learning research.  ...  Approaches range from utilizing structured knowledge in the training process of neural networks to enriching such architectures with ontological reasoning mechanisms.  ...  This is devoted in par- The edition of this special issue on Semantic Deep Learning has been supported by the German national project DeepLee, which is partially funded by the German Federal Ministry of  ... 
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A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation [article]

Yongjing Yin, Fandong Meng, Jinsong Su, Chulun Zhou, Zhengyuan Yang, Jie Zhou, Jiebo Luo
<span title="2020-07-17">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images.  ...  We then stack multiple graph-based multi-modal fusion layers that iteratively perform semantic interactions to learn node representations.  ...  Introduction Multi-modal neural machine translation (NMT) (Huang et al., 2016; has become an important research direction in machine translation, due to its research significance in multimodal deep learning  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.08742v1">arXiv:2007.08742v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/76mp6vmyrzczdfm2c6u5nlpaly">fatcat:76mp6vmyrzczdfm2c6u5nlpaly</a> </span>
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Deep Learning Techniques for Future Intelligent Cross-Media Retrieval [article]

Sadaqat ur Rehman, Muhammad Waqas, Shanshan Tu, Anis Koubaa, Obaid ur Rehman, Jawad Ahmad, Muhammad Hanif, Zhu Han
<span title="2020-07-21">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation  ...  The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems  ...  faced by multi-modal deep learning approaches with application to cross-media retrieval as shown in Fig. 7 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.01191v1">arXiv:2008.01191v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t63bg55w2vdqjcprzaaidrmprq">fatcat:t63bg55w2vdqjcprzaaidrmprq</a> </span>
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Introduction to the Special Issue on Deep Learning for Multi-Modal Intelligence Across Speech, Language, Vision, and Heterogeneous Signals

Xiaodong He, Li Deng, Richard Rose, Minlie Huang, Isabel Trancoso, Chao Zhang
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/aznf273kcvcbfjcdeghr3xjd6i" style="color: black;">IEEE Journal on Selected Topics in Signal Processing</a> </i> &nbsp;
progress has been made in artificial intelligence (AI) applications with single modality, such as speech recognition, speech synthesis, image classification, object detection, as well as machine translation  ...  Introduction to the Special Issue on Deep Learning for Multi-Modal Intelligence Across Speech, Language, Vision, and Heterogeneous Signals T HANKS to the disruptive advances in deep learning, significant  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jstsp.2020.2989852">doi:10.1109/jstsp.2020.2989852</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xlcv7jzkbvfirbbmrh4fpmtyxm">fatcat:xlcv7jzkbvfirbbmrh4fpmtyxm</a> </span>
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Multimodal Machine Learning: A Survey and Taxonomy [article]

Tadas Baltrušaitis, Chaitanya Ahuja, Louis-Philippe Morency
<span title="2017-08-01">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential.  ...  Multimodal machine learning aims to build models that can process and relate information from multiple modalities.  ...  TRANSLATION A big part of multimodal machine learning is concerned with translating (mapping) from one modality to another.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1705.09406v2">arXiv:1705.09406v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/262fo4sihffvxecg4nwsifoddm">fatcat:262fo4sihffvxecg4nwsifoddm</a> </span>
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Table of Contents [EDICS]

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rut5unc4enborm7fhpkwgeza7m" style="color: black;">IEEE/ACM Transactions on Audio Speech and Language Processing</a> </i> &nbsp;
Liu 2438 Machine Translation for Spoken and Written Language Neural Machine Translation With Noisy Lexical Constraints . . . . . . . . . . . . . . . . . . . . . H. Li, G. Huang, D. Cai, and L.  ...  Chen 1707 Multi-Source Neural Machine Translation With Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/taslp.2020.3046150">doi:10.1109/taslp.2020.3046150</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/easrxuwl6zdppejsrf4bskxfw4">fatcat:easrxuwl6zdppejsrf4bskxfw4</a> </span>
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Introduction to the special issue on deep learning approaches for machine translation

Marta R. Costa-jussà, Alexandre Allauzen, Loïc Barrault, Kyunghun Cho, Holger Schwenk
<span title="">2017</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fut52bg77zaztmwr37xhgokdce" style="color: black;">Computer Speech and Language</a> </i> &nbsp;
In the case of Machine Translation (MT), deep learning was first introduced in standard statistical systems. By now, end-to-end neural MT systems have reached competitive results.  ...  system; and introduction of deep learning in interactive MT and MT evaluation.  ...  Neural MT Context-On Interactive Neural MT Interactive Neural MT by Peris et al. MT Evaluation with deep learning MT Evaluation with Neural Networks by et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.csl.2017.03.001">doi:10.1016/j.csl.2017.03.001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qrep7sdnurfvnmoogz4hssnnh4">fatcat:qrep7sdnurfvnmoogz4hssnnh4</a> </span>
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Cross-modal Bidirectional Translation via Reinforcement Learning

Jinwei Qi, Yuxin Peng
<span title="">2018</span> <i title="International Joint Conferences on Artificial Intelligence Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vfwwmrihanevtjbbkti2kc3nke" style="color: black;">Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</a> </i> &nbsp;
Inspired by neural machine translation to establish a corresponding relationship between two entirely different languages, we attempt to treat images as a special kind of language to provide visual descriptions  ...  Experiments are conducted to verify the performance of our proposed approach on cross-modal retrieval, compared with 11 state-of-the-art methods on 3 datasets.  ...  Most of the recent works adopt deep neural network to achieve promising results in neural machine translation. Cho et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/365">doi:10.24963/ijcai.2018/365</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcai/QiP18.html">dblp:conf/ijcai/QiP18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pgdhqtog2zgafl6yekz7juudui">fatcat:pgdhqtog2zgafl6yekz7juudui</a> </span>
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Data fusion methods in multimodal human computer dialog

Ming-Hao YANG, Jian-Hua TAO
<span title="2019-02-01">2019</span> <i title="China Science Publishing &amp; Media Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrwdbi2denazdm5k6riguknyke" style="color: black;">Virtual Reality &amp; Intelligent Hardware</a> </i> &nbsp;
Intelligence methods of multi-modal information fusion in human-computer interaction SCIENTIA SINICA Informationis 48, 433 (2018); Rapid and High-quality 3D Fusion of Heterogeneous CT and MRI Data for  ...  We first introduce the cognitive assumption of single channel processing, and then discuss its implementation methods in human computer dialog; for the task of multi-modal information fusion, serval computing  ...  Generally speaking, multi-modal information fusion is applied to deep neural networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3724/sp.j.2096-5796.2018.0010">doi:10.3724/sp.j.2096-5796.2018.0010</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/journals/vrih/YangT19.html">dblp:journals/vrih/YangT19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jltufn3o6fd4pjzv4pnqd6wmvy">fatcat:jltufn3o6fd4pjzv4pnqd6wmvy</a> </span>
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Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [article]

Jing Zhang, Dacheng Tao
<span title="2020-11-17">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing.  ...  Next, we present a review of the progress in natural language processing (taking machine translation as an example) and multimedia and multi-modal analysis. 15) Machine Translation: Machine translation  ...  To respond to the dynamic environment, AIoT behaves via control and interaction, where deep learning has demonstrated its value in improving control accuracy and enabling multi-modal interactions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.08612v1">arXiv:2011.08612v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dflut2wdrjb4xojll34c7daol4">fatcat:dflut2wdrjb4xojll34c7daol4</a> </span>
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Advanced neural network systems for solving complex real problems

Olga Valenzuela, Fernando Rojas, Ignacio Rojas
<span title="2021-06-01">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rcs76v6h7jf2lnaoy2wi3gg4ny" style="color: black;">Neural Processing Letters</a> </i> &nbsp;
Mathematics for neural networks. RBF structures. Self-organizing networks and methods. Support vector machines and kernel methods. Fuzzy logic. Evolutionary and genetic algorithms. B Ignacio Rojas  ...  As in previous editions, we strongly emphasize the wide range of topics comprised under the umbrella of IWANN2019 and, in particular, we focus on trending topics such as Deep Learning and Big Data.  ...  The proposed methodology supports the translation of activity recognition capabilities between sensor modalities without user or system designer's intervention.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11063-021-10542-6">doi:10.1007/s11063-021-10542-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/64yftwndvvd3hf2gn75pr2oklm">fatcat:64yftwndvvd3hf2gn75pr2oklm</a> </span>
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Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception

Ruofei Ouyang, Bryan Kian Hsiang Low
<span title="2019-01-28">2019</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jmjdfeig5rayzgfuauvumileza" style="color: black;">Autonomous Robots</a> </i> &nbsp;
Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization Yijun Wang*, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Tie-Yan Liu, Guiquan Liu Dual-reference Face Retrieval  ...  Regularized Deep Networks Donghyun Yoo*, Haoqi Fan, Vishnu Bodetti, Kris Kitani Efficient Large-Scale Multi-Modal Classification Douwe Kiela*, Edouard Grave, Armand Joulin, Tomas Mikolov Efficient Multi-dimensional  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10514-018-09826-z">doi:10.1007/s10514-018-09826-z</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/67yqhwmgozccxni56rxmuapjgm">fatcat:67yqhwmgozccxni56rxmuapjgm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200319033824/https://aaai.org/Conferences/AAAI-18/wp-content/uploads/2017/12/AAAI-18-Accepted-Paper-List.Web_.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/9b/a39b2e42983c9863a5e6ebaad48c6bce360da7f3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10514-018-09826-z"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

New Ideas and Trends in Deep Multimodal Content Understanding: A Review

Wei Chen, Weiping Wang, Li Liu, Michael S. Lew
<span title="">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bby322qx6ndsje4ypr56c7nnly" style="color: black;">Neurocomputing</a> </i> &nbsp;
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text.  ...  Unlike classic reviews of deep learning where monomodal image classifiers such as VGG, ResNet and Inception module are central topics, this paper will examine recent multimodal deep models and structures  ...  With the powerful capabilities of deep neural networks, data from visual and textual modality can be represented as individual features using domain-specific neural networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neucom.2020.10.042">doi:10.1016/j.neucom.2020.10.042</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hyjkj5enozfrvgzxy6avtbmoxu">fatcat:hyjkj5enozfrvgzxy6avtbmoxu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715223135/http://jultika.oulu.fi/files/nbnfi-fe2021041910927.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/75/df/75df74eac92cb171ce31e8410e1aceeddbca872e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neucom.2020.10.042"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus [article]

Chen Wu, Ming Yan
<span title="2022-01-27">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose a novel deep semantic model which makes use of the utilities of not only the multi-modal sources, but also feature extractors such as self-attention, the aggregated vectors, combination  ...  Recently, deep neural network for code search has been a hot research topic.  ...  The first is the exploration of deep learning methods for semantic modeling. The second is the introduction of multi-modality latent interaction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.11313v1">arXiv:2201.11313v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/er5p53ejsbenjaywdppdrhtcyu">fatcat:er5p53ejsbenjaywdppdrhtcyu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220129183450/https://arxiv.org/pdf/2201.11313v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d7/9c/d79cdd21703d707396154bcc9ce0e2a8bb6bbd73.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.11313v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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