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Multimodal Machine Translation
2021
IEEE Access
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The results show that the model can significantly improve the quality of multimodal neural network machine translation, which also verifies the importance of integrating external knowledge and visual text ...
Besides, the decoder decodes and generates a translation based on the image and text representation of the source. ...
can significantly improve the quality of multimodal neural network machine translation. ...
doi:10.1109/access.2021.3115135
fatcat:d2anaeg3qnarpfmlc4eap2urrm
Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation
2017
arXiv
pre-print
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2017
GLU 2017 International Workshop on Grounding Language Understanding
unpublished
doi:10.21437/glu.2017-13 fatcat:mhj6yqsz6jb23gzsthsg6syrua
doi:10.21437/glu.2017-13 fatcat:mhj6yqsz6jb23gzsthsg6syrua
In Multimodal Neural Machine Translation (MNMT), a neural model generates a translated sentence that describes an image, given the image itself and one source descriptions in English. ...
We hypothesize that richer architecture, such as dense captioning models, may be more suitable for MNMT and could lead to improved translations. ...
Introduction In machine translation, neural networks have attracted a lot of research attention. Recently, the encoder-decoder framework [1] has been largely adopted. ...
arXiv:1707.01009v4
fatcat:kvj7idsk2bbexoyldtvwrezhha
Short Sequence Chinese-English Machine Translation Based on Generative Adversarial Networks of Emotion
2022
Computational Intelligence and Neuroscience
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How to improve the accuracy of neural machine translation through deep learning technology is the core problem that researchers study. ...
In this paper, the neural machine translation model based on generative adversarial network is studied to make the translation result of neural network more accurate and three-dimensional. ...
In addition to the Transformer model, there is still a lot of room for improvement in the neural machine translation model. ...
doi:10.1155/2022/3385477
pmid:35685136
pmcid:PMC9173932
fatcat:mfmd6t675vbwhbzjxbhgeurmxm
Encouraging an Appropriate Representation Simplifies Training of Neural Networks
[article]
2019
arXiv
pre-print
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internal representation may improve the generalization ability of neural networks. ...
A common assumption about neural networks is that they can learn an appropriate internal representations on their own, see e.g. end-to-end learning. In this work we challenge this assumption. ...
Buza was supported by Thematic Excellence Programme, Industry and Digitization Subprogramme, NRDI Office, 2019 and received the "Profes- ...
arXiv:1911.07245v1
fatcat:wabck324m5b7toq6e3mnnm2pbq
Neural Machine Translation for Cross-Lingual Pronoun Prediction
2017
Proceedings of the Third Workshop on Discourse in Machine Translation
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For all four language pairs, we trained a standard attention-based neural machine translation system as well as three variants that incorporate information from the preceding source sentence. ...
We show that our systems, which are not specifically designed for pronoun prediction and may be used to generate complete sentence translations, generally achieve competitive results on this task. * This ...
Acknowledgments This work was supported by Samsung Electronics ("Larger-Context Neural Machine Translation" and "Next Generation Deep Learning: from pattern recognition to AI"). ...
doi:10.18653/v1/w17-4806
dblp:conf/discomt/JeanLFC17
fatcat:vr4aci5vr5hcvmo54witfr3kxa
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
[article]
2014
arXiv
pre-print
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The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature ...
One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. ...
Furthermore, in
Word and Phrase Representations Since the proposed RNN Encoder-Decoder is not specifically designed only for the task of machine translation, here we briefly look at the properties of ...
arXiv:1406.1078v3
fatcat:5gl2ci3wbnagzgbe5mtlqh6guu
A Comparative Study of Text Genres in English-Chinese Translation Effects Based on Deep Learning LSTM
2022
Computational and Mathematical Methods in Medicine
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Simultaneously, grammar knowledge is essential for translation, as it aids in the grammatical representation of word sequences and reduces grammatical errors. ...
In recent years, neural network-based English-Chinese translation models have gradually supplanted traditional translation methods. ...
graph representation by potential graph parsing, and using source and target-side dependency tree to improve neural machine translation. ...
doi:10.1155/2022/7068406
pmid:35693269
pmcid:PMC9184169
fatcat:wy7tzar6end7hndajf25octb2q
Encouraging an appropriate representation simplifies training of neural networks
2020
Acta Universitatis Sapientiae: Informatica
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internal representation may improve the generalization ability of neural networks. ...
AbstractA common assumption about neural networks is that they can learn an appropriate internal representation on their own, see e.g. end-to-end learning. In this work we challenge this assumption. ...
ED 18-1-2019-0030 (Application domain specific highly reliable IT solutions subprogramme) has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary ...
doi:10.2478/ausi-2020-0007
fatcat:tqlejerk5zg2hajn6hgj6dquji
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
2014
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
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The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature ...
One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. ...
Furthermore, in
Word and Phrase Representations Since the proposed RNN Encoder-Decoder is not specifically designed only for the task of machine translation, here we briefly look at the properties of ...
doi:10.3115/v1/d14-1179
dblp:conf/emnlp/ChoMGBBSB14
fatcat:uiy743kyojcknh7pjgs4x33osa
Encoders Help You Disambiguate Word Senses in Neural Machine Translation
[article]
2019
arXiv
pre-print
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Neural machine translation (NMT) has achieved new state-of-the-art performance in translating ambiguous words. However, it is still unclear which component dominates the process of disambiguation. ...
We train a classifier to predict whether a translation is correct given the representation of an ambiguous noun. ...
Neural machine translation by jointly ing Representations, San Diego, California, USA.
learning to align and translate. ...
arXiv:1908.11771v1
fatcat:madqd2eqbnfinabvhs2etkuvfy
Research on Neural Machine Translation Model
2019
Journal of Physics, Conference Series
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modeling and transduction problems for a long time, such as language modeling and machine translation. ...
In neural machine translation (NMT), cyclic neural networks, especially long-term and short-term memory networks and gated recurrent neural networks, have been regarded as the latest methods for sequence ...
Acknowledgments we express our sincere gratitude to Teacher Li Yong for his help in the process of writing the thesis. ...
doi:10.1088/1742-6596/1237/5/052020
fatcat:nghf3oryznatboysa2t4xswlmu
Toward English-Chinese Translation Based on Neural Networks
2022
Mobile Information Systems
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Toward this solation, a neural network (NN) based translation approach is proposed to predict word order differences in language translation and improve translation accuracy in long sentences. ...
Experimental results show that the NN preorder model can significantly improve translation accuracy and system performance. ...
This is similar to human translation work, that is, first understand the meaning of the source language and then organize the language for translation. ...
doi:10.1155/2022/3114123
fatcat:p7bpxsgd2fc4hfe2yfm7rlffha
What do Neural Machine Translation Models Learn about Morphology?
2017
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. ...
In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic ...
Acknowledgments We would like to thank Helmut Schmid for providing the Tiger corpus, members of the MIT Spoken Language Systems group for helpful comments, and the three anonymous reviewers for their useful ...
doi:10.18653/v1/p17-1080
dblp:conf/acl/BelinkovDDSG17
fatcat:55clgjs6vnff3irc77aqtomhwu
Research on Machine Translation of Deep Neural Network Learning Model Based on Ontology
2021
Informatica (Ljubljana, Tiskana izd.)
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The machine translation method based on deep neural network learning can significantly improve the quality and efficiency of translation. ...
According to the characteristics of Ontology's domain knowledge concept system, deep neural network learning model based machine translation method is proposed. ...
Acknowledgement Research Center of Shangluo Culture and Jia Pingwa, A Study on the Translation and Introduction of Mo Yan's Works and Jia Pingwa's Works in English-speaking Countries (18SLWH05). ...
doi:10.31449/inf.v45i5.3559
fatcat:4rrkga4l2jeerdy7nens6pt7he
Hard but Robust, Easy but Sensitive: How Encoder and Decoder Perform in Neural Machine Translation
[article]
2019
arXiv
pre-print
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Neural machine translation (NMT) typically adopts the encoder-decoder framework. ...
A good understanding of the characteristics and functionalities of the encoder and decoder can help to explain the pros and cons of the framework, and design better models for NMT. ...
Yanzhuo Ding, Yang Liu, Huanbo Luan, and Maosong
Sun. 2017. Visualizing and understanding neural
machine translation. ...
arXiv:1908.06259v1
fatcat:6bvoj2conff5dft2gckrab2xrm
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