scholarly journals IntroVNMT: An Introspective Model for Variational Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 8830-8837
Author(s):  
Xin Sheng ◽  
Linli Xu ◽  
Junliang Guo ◽  
Jingchang Liu ◽  
Ruoyu Zhao ◽  
...  
Keyword(s):  
Machine Translation ◽  
Latent Variables ◽  
Image Synthesis ◽  
Target Language ◽  
Generative Adversarial Network ◽  
Neural Machine Translation ◽  
Adversarial Network ◽  
Proposed Model ◽  
Model Training ◽  
High Level

We propose a novel introspective model for variational neural machine translation (IntroVNMT) in this paper, inspired by the recent successful application of introspective variational autoencoder (IntroVAE) in high quality image synthesis. Different from the vanilla variational NMT model, IntroVNMT is capable of improving itself introspectively by evaluating the quality of the generated target sentences according to the high-level latent variables of the real and generated target sentences. As a consequence of introspective training, the proposed model is able to discriminate between the generated and real sentences of the target language via the latent variables generated by the encoder of the model. In this way, IntroVNMT is able to generate more realistic target sentences in practice. In the meantime, IntroVNMT inherits the advantages of the variational autoencoders (VAEs), and the model training process is more stable than the generative adversarial network (GAN) based models. Experimental results on different translation tasks demonstrate that the proposed model can achieve significant improvements over the vanilla variational NMT model.

scholarly journals Machine Translation System Using Deep Learning for English to Urdu

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Syed Abdul Basit Andrabi ◽  
Abdul Wahid
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Communication Technology ◽  
Target Language ◽  
Translation System ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Proposed Model ◽  
Learning Technique ◽  
Machine Translation System

Machine translation is an ongoing field of research from the last decades. The main aim of machine translation is to remove the language barrier. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages. Parallel corpus sizes of around 30923 sentences are used. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The proposed model has an average BLEU score of 45.83.

scholarly journals Unsupervised Generation and Synthesis of Facial Images via an Auto-Encoder-Based Deep Generative Adversarial Network

2020 ◽  
Vol 10 (6) ◽  
pp. 1995 ◽  
Author(s):  
Jeong gi Kwak ◽  
Hanseok Ko
Keyword(s):  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Training Scheme ◽  
Adversarial Networks ◽  
High Level ◽  
Facial Images ◽  
And Training ◽  
Double Constraint

The processing of facial images is an important task, because it is required for a large number of real-world applications. As deep-learning models evolve, they require a huge number of images for training. In reality, however, the number of images available is limited. Generative adversarial networks (GANs) have thus been utilized for database augmentation, but they suffer from unstable training, low visual quality, and a lack of diversity. In this paper, we propose an auto-encoder-based GAN with an enhanced network structure and training scheme for Database (DB) augmentation and image synthesis. Our generator and decoder are divided into two separate modules that each take input vectors for low-level and high-level features; these input vectors affect all layers within the generator and decoder. The effectiveness of the proposed method is demonstrated by comparing it with baseline methods. In addition, we introduce a new scheme that can combine two existing images without the need for extra networks based on the auto-encoder structure of the discriminator in our model. We add a novel double-constraint loss to make the encoded latent vectors equal to the input vectors.

scholarly journals Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior

2020 ◽  
Vol 34 (05) ◽  
pp. 8846-8853 ◽  
Author(s):  
Raphael Shu ◽  
Jason Lee ◽  
Hideki Nakayama ◽  
Kyunghyun Cho
Keyword(s):  
Machine Translation ◽  
Latent Variables ◽  
Latent Variable ◽  
Target Sequence ◽  
Inference Algorithm ◽  
Inference Procedure ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Proposed Model ◽  
Recent Refinement

Although neural machine translation models reached high translation quality, the autoregressive nature makes inference difficult to parallelize and leads to high translation latency. Inspired by recent refinement-based approaches, we propose LaNMT, a latent-variable non-autoregressive model with continuous latent variables and deterministic inference procedure. In contrast to existing approaches, we use a deterministic inference algorithm to find the target sequence that maximizes the lowerbound to the log-probability. During inference, the length of translation automatically adapts itself. Our experiments show that the lowerbound can be greatly increased by running the inference algorithm, resulting in significantly improved translation quality. Our proposed model closes the performance gap between non-autoregressive and autoregressive approaches on ASPEC Ja-En dataset with 8.6x faster decoding. On WMT'14 En-De dataset, our model narrows the gap with autoregressive baseline to 2.0 BLEU points with 12.5x speedup. By decoding multiple initial latent variables in parallel and rescore using a teacher model, the proposed model further brings the gap down to 1.0 BLEU point on WMT'14 En-De task with 6.8x speedup.

scholarly journals Context-Aware Neural Machine Translation for Korean Honorific Expressions

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1589
Author(s):  
Yongkeun Hwang ◽  
Yanghoon Kim ◽  
Kyomin Jung
Keyword(s):  
Machine Translation ◽  
Deep Neural Networks ◽  
Contextual Information ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Sentence Level ◽  
Proposed Model ◽  
The Given ◽  
The Relationship

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

scholarly journals CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

2021 ◽  
Vol 11 (4) ◽  
pp. 1380
Author(s):  
Yingbo Zhou ◽  
Pengcheng Zhao ◽  
Weiqin Tong ◽  
Yongxin Zhu
Keyword(s):  
Distance Learning ◽  
Feature Learning ◽  
Image Synthesis ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

scholarly journals Controlling Neural Machine Translation Formality with Synthetic Supervision

2020 ◽  
Vol 34 (05) ◽  
pp. 8568-8575
Author(s):  
Xing Niu ◽  
Marine Carpuat
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Sentence Pair ◽  
English Sentence ◽  
Neural Machine Translation ◽  
Source Language ◽  
Training Scheme ◽  
Training Examples ◽  
Language Content ◽  
Missing Element

This work aims to produce translations that convey source language content at a formality level that is appropriate for a particular audience. Framing this problem as a neural sequence-to-sequence task ideally requires training triplets consisting of a bilingual sentence pair labeled with target language formality. However, in practice, available training examples are limited to English sentence pairs of different styles, and bilingual parallel sentences of unknown formality. We introduce a novel training scheme for multi-task models that automatically generates synthetic training triplets by inferring the missing element on the fly, thus enabling end-to-end training. Comprehensive automatic and human assessments show that our best model outperforms existing models by producing translations that better match desired formality levels while preserving the source meaning.1

scholarly journals Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4291
Author(s):  
Xuejiao Gong ◽  
Bo Tang ◽  
Ruijin Zhu ◽  
Wenlong Liao ◽  
Like Song
Keyword(s):  
Latent Variables ◽  
Data Augmentation ◽  
Sampling Technique ◽  
Smart Meters ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Electricity Theft ◽  
Adversarial Network ◽  
Low Dimensional ◽  
Power Curves

Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection. Therefore, a data augmentation method for electricity theft detection based on the conditional variational auto-encoder (CVAE) is proposed. Firstly, the stealing power curves are mapped into low dimensional latent variables by using the encoder composed of convolutional layers, and the new stealing power curves are reconstructed by the decoder composed of deconvolutional layers. Then, five typical attack models are proposed, and the convolutional neural network is constructed as a classifier according to the data characteristics of stealing power curves. Finally, the effectiveness and adaptability of the proposed method is verified by a smart meters’ data set from London. The simulation results show that the CVAE can take into account the shapes and distribution characteristics of samples at the same time, and the generated stealing power curves have the best effect on the performance improvement of the classifier than the traditional augmentation methods such as the random oversampling method, synthetic minority over-sampling technique, and conditional generative adversarial network. Moreover, it is suitable for different classifiers.

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