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Compact Personalized Models for Neural Machine Translation

Joern Wuebker, Patrick Simianer, John DeNero
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
We propose and compare methods for gradientbased domain adaptation of self-attentive neural machine translation models.  ...  Our system architecture-combining a state-of-the-art self-attentive model with compact domain adaptation-provides high quality personalized machine translation that is both space and time efficient.  ...  Self-Attentive Translation Model The neural machine translation systems used in this work are based on the Transformer model introduced by Vaswani et al. (2017) , which uses selfattention rather than  ... 
doi:10.18653/v1/d18-1104 dblp:conf/emnlp/WuebkerSD18 fatcat:fqw7nvhtirhuzmkbf6gid3fuc4

Compact Personalized Models for Neural Machine Translation [article]

Joern Wuebker, Patrick Simianer, John DeNero
2018 arXiv   pre-print
We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models.  ...  Our system architecture - combining a state-of-the-art self-attentive model with compact domain adaptation - provides high quality personalized machine translation that is both space and time efficient  ...  Self-Attentive Translation Model The neural machine translation systems used in this work are based on the Transformer model introduced by Vaswani et al. (2017) , which uses selfattention rather than  ... 
arXiv:1811.01990v1 fatcat:eghdtqrk7rbv3hjeyiiypw6skq

Turning NMT Research into Commercial Products

Dragos Munteanu, Adrià de Gispert
2018 Conference of the Association for Machine Translation in the Americas  
"Neural Machine Translation by Minimising the Bayes-risk with Respect to Syntactic Translation Lattices" • Control is must for commercial success • One very bad sentence can put off a customer COMPARING  ...  • Discriminative training • Reliance on GPUs • Target-side-guided decoding • Smaller compact models Better translation models [Zhou et al.'16] [Gehring et al.'17] [Vaswani et al.'17] [Sutskever et al.  ...  Data is EVEN MORE important • New NMT models are better learners  ... 
dblp:conf/amta/MunteanuG18 fatcat:wmja6l7yyvgmhd5b3anb54z324

Analyzing Knowledge Distillation in Neural Machine Translation

Dakun Zhang, Josep Maria Crego, Jean Senellart
2018 International Workshop on Spoken Language Translation  
Knowledge distillation has recently been successfully applied to neural machine translation.  ...  It allows for building shrunk networks while the resulting systems retain most of the quality of the original model.  ...  Thus, a compact smaller model is generated and used to replace the larger model, especially in some resource limited devices. For machine translation, we follow the approach described by [6] .  ... 
dblp:conf/iwslt/ZhangCS18 fatcat:5ekww7p4wzarbeplfmxrlx6kdi

The AFRL-MITLL WMT16 News-Translation Task Systems

Jeremy Gwinnup, Tim Anderson, Grant Erdmann, Katherine Young, Michaeel Kazi, Elizabeth Salesky, Brian Thompson
2016 Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers  
New techniques applied this year include Neural Machine Translation, a unique selection process for language modelling data, additional out-of-vocabulary transliteration techniques, and morphology generation  ...  This paper describes the AFRL-MITLL statistical machine translation systems and the improvements that were developed during the WMT16 evaluation campaign.  ...  Acknowledgements We wish to thank the anonymous reviewers for their comments and insight. References  ... 
doi:10.18653/v1/w16-2313 dblp:conf/wmt/GwinnupAEYKST16 fatcat:klv26gqvivf3jar5avtjl3555e

Design of English Translation Mobile Information System Based on Recurrent Neural Network

Yue Gao, Wen Zhang
2022 Mobile Information Systems  
To solve the problem of translating lines of difference in length into English, this article presents a model of neural network recovery (RNN) English translator-based models of end-to-end encoder-decoder  ...  Summary. the English translation model based on the neural repetitive fusion is efficient and stable.  ...  Neural machine translation outperformed phrasebased statistical machine translation for 27 language pairs. [5]. Poncelas et al first proposed the case-based machine translation method.  ... 
doi:10.1155/2022/8053285 fatcat:vz5jfkgagnhf7luqrb3pf26yqy

Removing European Language Barriers with Innovative Machine Translation Technology

Dario Franceschini, Chiara Canton, Ivan Simonini, Armin Schweinfurth, Adelheid Glott, Sebastian Stüker, Thai-Son Nguyen, Felix Schneider, Thanh-Le Ha, Alex Waibel, Barry Haddow, Philip Williams (+5 others)
2020 International Conference on Language Resources and Evaluation  
This paper presents our progress towards deploying a versatile communication platform in the task of highly multilingual live speech translation for conferences and remote meetings live subtitling.  ...  While for decades systems for speech recognition and machine translation where based on Bayes' rule and made use of statistical methods such as Hidden Markov Models, Gaussian Mixture Models, N-Gram Models  ...  automatic speech recognition, machine translation etc. with a single neural network architecture, instead of solving the problem with several models given by Bayes' rule.  ... 
dblp:conf/lrec/FranceschiniCSS20 fatcat:4v7cxbs73nh4bozcpncsczensq

Learning to Fix Build Errors with Graph2Diff Neural Networks

Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
2020 Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops  
used for machine translation.  ...  Recently, the DeepDelta system [10] aimed to repair build errors by applying neural machine translation (NMT), translating the text of the diagnostic message to a description of the repair.  ... 
doi:10.1145/3387940.3392181 dblp:conf/icse/TarlowMRCMSA20 fatcat:grwcc7233jb2jpyqksos3pqyba

Understanding neural networks with neural-symbolic integration

2021 Research Outreach  
The researchers have developed solutions for both approaches for a type of machine learning paradigm called the Convolutional Neural Network (CNN).  ...  The convolutional neural network is a type of machine learning paradigm.  ...  Personal Response What are the main challenges involved in automating the labelling process for filters/symbols in both ERIC and EBP?  ... 
doi:10.32907/ro-126-1906089938 fatcat:tjlmpt7pobarzbmwoukg4mmxme

Conversation Engine for Deaf and Dumb

Monika K J
2021 International Journal for Research in Applied Science and Engineering Technology  
Development of linguistic communication recognition application for deaf people is vital, as they'll be able to communicate easily with even people who don't understand language.  ...  To speak with others people with disability use different modes, there are number of methods available for his or her communication one such common method of communication is linguistic communication.  ...  For supervised learning tasks, deep learning strategies eliminate feature engineering, by translating the data into compact intermediate representations principal elements, and derive bedded structures  ... 
doi:10.22214/ijraset.2021.36841 fatcat:pu4dckm5e5ft7npznebe2csl2m

Sign Language Detection using LSTM Deep Learning Model (Action Recognition with Python )

Sammon Babu, Grace Joseph
2022 Zenodo  
They are processed through a neural network using transfer learning to help the machine "learn" what is being signed after already having been taught on larger datasets of many more images and classifications  ...  The reason for such a project is to help diminish the gap between those who can hear well and those hard of hearing or even deaf.  ...  The study [5] , compared various conventional machine learning and deep learning models to classify American sign language.  ... 
doi:10.5281/zenodo.6905809 fatcat:np7ki7n3cbffxkcggatgeq7p2u

VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research [article]

Xin Wang, Jiawei Wu, Junkun Chen, Lei Li, Yuan-Fang Wang, William Yang Wang
2020 arXiv   pre-print
We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model,  ...  and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.  ...  Video-guided Machine Translation (VMT) In Figure 13 , we showcase the advantages of the VMT model over the base neural machine translation (NMT) model.  ... 
arXiv:1904.03493v3 fatcat:mo5gwbvjhza5thhftq6ivyehci

AGE ESTIMATION USING NEURAL NETWORKS BASED ON FACE IMAGES WITH STUDY OF DIFFERENT FEATURE EXTRACTION METHODS

Rajan Vishnu Parab, Meenal Suryakant Vatsaraj, D.S. Bade
2017 International Journal of Students Research in Technology & Management  
These extracted features are classified using support vector machine and artificial neural network.  ...  Degree of accuracy for age estimation is obtained by forming appropriate feature vector of a facial image. Feature vectors are constructed from facial features.  ...  Zhang [4] says, active appearance model consist face images in a sequence of age ascending for same person.  ... 
doi:10.18510/ijsrtm.2017.526 fatcat:ta525nu36nd3lf35hr5l5gyiga

Neural Approaches to Conversational AI

Jianfeng Gao, Michel Galley, Lihong Li
2018 Proceedings of ACL 2018, Tutorial Abstracts  
For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and  ...  challenges we are facing, using specific systems and models as case studies.  ...  Part 2 (45 minutes): QA and MRC • The KB-QA task • Semantic parsing • Embedding-based KB-QA • Multi-turn KB-QA agents • Machine reading for Text-QA • Neural MRC models • QA in Bing 3.  ... 
doi:10.18653/v1/p18-5002 dblp:conf/acl/GaoGL18 fatcat:7llxwuntafh4fcjj4ia3tm642a

Learning Kernel-Smoothed Machine Translation with Retrieved Examples [article]

Qingnan Jiang, Mingxuan Wang, Jun Cao, Shanbo Cheng, Shujian Huang, Lei Li
2021 arXiv   pre-print
How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining?  ...  Despite the great success of neural machine translation, updating the deployed models online remains a challenge.  ...  Compact personalized models for neural ma- chine translation. In Proceedings of the 2018 Con- Michael McCloskey and Neal J. Cohen. 1989.  ... 
arXiv:2109.09991v2 fatcat:tkynje7j3nfo3erf5nl27osvka
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