scholarly journals Translation Mechanism of Neural Machine Algorithm for Online English Resources

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanping Ye
Keyword(s):  
Data Processing ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Processing Methods ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Sentence Similarity ◽  
Alignment Structure ◽  
Data Processing Methods

At the level of English resource vocabulary, due to the lack of vocabulary alignment structure, the translation of neural machine translation has the problem of unfaithfulness. This paper proposes a framework that integrates vocabulary alignment structure for neural machine translation at the vocabulary level. Under the proposed framework, the neural machine translation decoder receives external vocabulary alignment information during each step of the decoding process to further alleviate the problem of missing vocabulary alignment structure. Specifically, this article uses the word alignment structure of statistical machine translation as the external vocabulary alignment information and introduces it into the decoding step of neural machine translation. The model is mainly based on neural machine translation, and the statistical machine translation vocabulary alignment structure is integrated on the basis of neural networks and continuous expression of words. In the model decoding stage, the statistical machine translation system provides appropriate vocabulary alignment information based on the decoding information of the neural machine translation and recommends vocabulary based on the vocabulary alignment information to guide the neural machine translation decoder to more accurately estimate its vocabulary in the target language. From the aspects of data processing methods and machine translation technology, experiments are carried out to compare the data processing methods based on language model and sentence similarity and the effectiveness of machine translation models based on fusion principles. Comparative experiment results show that the data processing method based on language model and sentence similarity effectively guarantees data quality and indirectly improves the algorithm performance of machine translation model; the translation effect of neural machine translation model integrated with statistical machine translation vocabulary alignment structure is compared with other models.

scholarly journals Sulis: An Open Source Transfer Decoder for Deep Syntactic Statistical Machine Translation

2010 ◽  
Vol 93 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Yvette Graham
Keyword(s):  
Open Source ◽  
Linear Combination ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Beam Search ◽  
Translation Model ◽  
Transfer Rules ◽  
Log Linear

Sulis: An Open Source Transfer Decoder for Deep Syntactic Statistical Machine Translation In this paper, we describe an open source transfer decoder for Deep Syntactic Transfer-Based Statistical Machine Translation. Transfer decoding involves the application of transfer rules to a SL structure. The N-best TL structures are found via a beam search of TL hypothesis structures which are ranked via a log-linear combination of feature scores, such as translation model and dependency-based language model.

Intelligent English translation system based on evolutionary multi-objective optimization algorithm

2020 ◽  
pp. 1-11
Author(s):  
Xin Song
Keyword(s):  
Data Processing ◽  
Machine Translation ◽  
Optimization Algorithm ◽  
Learning Algorithm ◽  
Translation System ◽  
Multi Objective Optimization ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Multi Objective ◽  
Model Training

The difficulty of obtaining the characteristics of the corpus database of neural machine translation is a factor hindering its development. In order to improve the effect of English intelligent translation, based on the machine learning algorithm, this paper improves the multi-objective optimization algorithm to construct a model based on the English intelligent translation system. Moreover, this paper uses parallel corpus and monolingual corpus for model training and uses semi-supervised neural machine translation method to analyze the data processing path in detail and focuses on the analysis of node distribution and data processing flow. In addition, this paper introduces data-related regularization items through the probabilistic nature of the neural machine translation model and applies it to the monolingual corpus to help the training of the neural machine translation model. Finally, this paper designs experiments to verify the performance of this model. The research results show that the translation model constructed in this paper is highly intelligent and can meet actual translation needs.

scholarly journals Improving the role of language model in statistical machine translation (Indonesian-Javanese)

2020 ◽  
Vol 10 (2) ◽  
pp. 2102
Author(s):  
Herry Sujaini
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Language Models ◽  
Standard Language ◽  
Translation Model ◽  
Model Research ◽  
Baseline System ◽  
Translation Machine

The statistical machine translation (SMT) is widely used by researchers and practitioners in recent years. SMT works with quality that is determined by several important factors, two of which are language and translation model. Research on improving the translation model has been done quite a lot, but the problem of optimizing the language model for use on machine translators has not received much attention. On translator machines, language models usually use trigram models as standard. In this paper, we conducted experiments with four strategies to analyze the role of the language model used in the Indonesian-Javanese translation machine and show improvement compared to the baseline system with the standard language model. The results of this research indicate that the use of 3-gram language models is highly recommended in SMT.

scholarly journals Statistical Machine Translation Pada Bahasa Lampung Dialek Api Ke Bahasa Indonesia

2020 ◽  
Vol 4 (3) ◽  
pp. 519
Author(s):  
Permata Permata ◽  
Zaenal Abidin
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Translation Model ◽  
Parallel Corpus ◽  
Automatic Translation ◽  
Initial Stage ◽  
Evaluation Phase ◽  
Parallel Body ◽  
Bahasa Indonesia

In this research, automatic translation of the Lampung dialect into Indonesian was carried out using the statistical machine translation (SMT) approach. Translation of the Lampung language to Indonesian can be done by using a dictionary. Another alternative is to use the Lampung parallel body corpus and its translation in Indonesian with the SMT approach. The SMT approach is carried out in several phases. Starting from the pre-processing phase which is the initial stage to prepare a parallel corpus. Then proceed with the training phase, namely the parallel corpus processing phase to obtain a language model and translation model. Then the testing phase, and ends with the evaluation phase. SMT testing uses 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 compound sentences without OOV and 25 compound sentences with OOV. The results of testing the translation of Lampung sentences into Indonesian shows the accuracy of the Bilingual Evaluation Undestudy (BLEU) obtained is 77.07% in 25 single sentences without out-of-vocabulary (OOV), 72.29% in 25 single sentences with OOV, 79.84% at 25 compound sentences without OOV and 80.84% at 25 compound sentences with OOV.

scholarly journals Keeping Models Consistent between Pretraining and Translation for Low-Resource Neural Machine Translation

2020 ◽  
Vol 12 (12) ◽  
pp. 215
Author(s):  
Wenbo Zhang ◽  
Xiao Li ◽  
Yating Yang ◽  
Rui Dong ◽  
Gongxu Luo
Keyword(s):  
Machine Translation ◽  
Language Model ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Parallel Corpus ◽  
Model Experiments ◽  
Low Resource ◽  
Translation Quality ◽  
Number Of Layers ◽  
Cross Lingual

Recently, the pretraining of models has been successfully applied to unsupervised and semi-supervised neural machine translation. A cross-lingual language model uses a pretrained masked language model to initialize the encoder and decoder of the translation model, which greatly improves the translation quality. However, because of a mismatch in the number of layers, the pretrained model can only initialize part of the decoder’s parameters. In this paper, we use a layer-wise coordination transformer and a consistent pretraining translation transformer instead of a vanilla transformer as the translation model. The former has only an encoder, and the latter has an encoder and a decoder, but the encoder and decoder have exactly the same parameters. Both models can guarantee that all parameters in the translation model can be initialized by the pretrained model. Experiments on the Chinese–English and English–German datasets show that compared with the vanilla transformer baseline, our models achieve better performance with fewer parameters when the parallel corpus is small.

scholarly journals Improving Transformer-Based Neural Machine Translation with Prior Alignments

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Thien Nguyen ◽  
Lam Nguyen ◽  
Phuoc Tran ◽  
Huu Nguyen
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
The Novel ◽  
Reverse Effect ◽  
Human Judgment ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Type Selection ◽  
Transformer Model ◽  
Positive Effect

Transformer is a neural machine translation model which revolutionizes machine translation. Compared with traditional statistical machine translation models and other neural machine translation models, the recently proposed transformer model radically and fundamentally changes machine translation with its self-attention and cross-attention mechanisms. These mechanisms effectively model token alignments between source and target sentences. It has been reported that the transformer model provides accurate posterior alignments. In this work, we empirically prove the reverse effect, showing that prior alignments help transformer models produce better translations. Experiment results on Vietnamese-English news translation task show not only the positive effect of manually annotated alignments on transformer models but also the surprising outperformance of statistically constructed alignments reinforced with the flexibility of token-type selection over manual alignments in improving transformer models. Statistically constructed word-to-lemma alignments are used to train a word-to-word transformer model. The novel hybrid transformer model improves the baseline transformer model and transformer model trained with manual alignments by 2.53 and 0.79 BLEU, respectively. In addition to BLEU score, we make limited human judgment on translation results. Strong correlation between human and machine judgment confirms our findings.

ANALYSIS OF THE FLOODING SYSTEM WITH APPLICATION OF STATISTICAL DATA PROCESSING METHODS

2020 ◽  
pp. 5-9
Author(s):  
A.Yu. Sentsov ◽  
◽  
I.V. Ryabov ◽  
A.A. Ankudinov ◽  
Yu.E. Radevich ◽  
...  
Keyword(s):  
Data Processing ◽  
Statistical Data ◽  
Processing Methods ◽  
Statistical Data Processing ◽  
Data Processing Methods

Use of Mathematical Modeling in Building Ceramics Functional Properties Studies

2016 ◽  
Vol 870 ◽  
pp. 191-195
Author(s):  
N.A. Vil'bitskaya ◽  
S.A. Vilbitsky ◽  
A.G. Avakyan
Keyword(s):  
Mathematical Modeling ◽  
Data Processing ◽  
Ceramic Material ◽  
Functional Properties ◽  
Statistical Data ◽  
Ceramic Tiles ◽  
Processing Methods ◽  
Statistical Data Processing ◽  
High Calcium ◽  
Data Processing Methods

The peculiarities of using mathematical and statistical data processing methods in studying the intensification in the process of sintering a ceramic material with a high content of high-calcium waste, and mineralizing sintering lithium-containing waste were studied. The region of optimal ceramic masses composition, which allows obtaining ceramic tiles with high functional properties, was defined.

Surface movement radar data processing methods for airport surveillance

2001 ◽  
Vol 37 (2) ◽  
pp. 563-585 ◽  
Author(s):  
J. Garcia Herrero ◽  
J.A. Besada Portas ◽  
F.J. Jimenez Rodriguez ◽  
J.R. Casar Corredera
Keyword(s):  
Data Processing ◽  
Radar Data ◽  
Processing Methods ◽  
Surface Movement ◽  
Radar Data Processing ◽  
Data Processing Methods
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