scholarly journals English Machine Translation Model Based on an Improved Self-Attention Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenxia Pan
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Extraction Methods ◽  
Attention Mechanism ◽  
The Self ◽  
Position Information ◽  
Directional Information ◽  
Translation Model ◽  
Model Based ◽  
Phrase Extraction

English machine translation is a natural language processing research direction that has important scientific research value and practical value in the current artificial intelligence boom. The variability of language, the limited ability to express semantic information, and the lack of parallel corpus resources all limit the usefulness and popularity of English machine translation in practical applications. The self-attention mechanism has received a lot of attention in English machine translation tasks because of its highly parallelizable computing ability, which reduces the model’s training time and allows it to capture the semantic relevance of all words in the context. The efficiency of the self-attention mechanism, however, differs from that of recurrent neural networks because it ignores the position and structure information between context words. The English machine translation model based on the self-attention mechanism uses sine and cosine position coding to represent the absolute position information of words in order to enable the model to use position information between words. This method, on the other hand, can reflect relative distance but does not provide directionality. As a result, a new model of English machine translation is proposed, which is based on the logarithmic position representation method and the self-attention mechanism. This model retains the distance and directional information between words, as well as the efficiency of the self-attention mechanism. Experiments show that the nonstrict phrase extraction method can effectively extract phrase translation pairs from the n-best word alignment results and that the extraction constraint strategy can improve translation quality even further. Nonstrict phrase extraction methods and n-best alignment results can significantly improve the quality of translation translations when compared to traditional phrase extraction methods based on single alignment.

PhraseAttn: Dynamic Slot Capsule Networks for phrase representation in Neural Machine Translation

2021 ◽  
pp. 1-8
Author(s):  
Binh Nguyen ◽  
Binh Le ◽  
Long H.B. Nguyen ◽  
Dien Dinh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Vital Role ◽  
Attention Mechanism ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Word Representation

 Word representation plays a vital role in most Natural Language Processing systems, especially for Neural Machine Translation. It tends to capture semantic and similarity between individual words well, but struggle to represent the meaning of phrases or multi-word expressions. In this paper, we investigate a method to generate and use phrase information in a translation model. To generate phrase representations, a Primary Phrase Capsule network is first employed, then iteratively enhancing with a Slot Attention mechanism. Experiments on the IWSLT English to Vietnamese, French, and German datasets show that our proposed method consistently outperforms the baseline Transformer, and attains competitive results over the scaled Transformer with two times lower parameters.

scholarly journals Dual contextual module for neural machine translation

2021 ◽  
Author(s):  
Isaac Kojo Essel Ampomah ◽  
Sally McClean ◽  
Glenn Hawe
Keyword(s):  
Machine Translation ◽  
Contextual Information ◽  
Attention Mechanism ◽  
The Self ◽  
Experimental Results ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Global Context ◽  
Overall Performance ◽  
Transformer Model

AbstractSelf-attention-based encoder-decoder frameworks have drawn increasing attention in recent years. The self-attention mechanism generates contextual representations by attending to all tokens in the sentence. Despite improvements in performance, recent research argues that the self-attention mechanism tends to concentrate more on the global context with less emphasis on the contextual information available within the local neighbourhood of tokens. This work presents the Dual Contextual (DC) module, an extension of the conventional self-attention unit, to effectively leverage both the local and global contextual information. The goal is to further improve the sentence representation ability of the encoder and decoder subnetworks, thus enhancing the overall performance of the translation model. Experimental results on WMT’14 English-German (En$$\rightarrow $$ → De) and eight IWSLT translation tasks show that the DC module can further improve the translation performance of the Transformer model.

scholarly journals CWPC_BiAtt: Character–Word–Position Combined BiLSTM-Attention for Chinese Named Entity Recognition

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 45 ◽  
Author(s):  
Shardrom Johnson ◽  
Sherlock Shen ◽  
Yuanchen Liu
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Attention Mechanism ◽  
Entity Recognition ◽  
Position Information ◽  
Named Entity ◽  
Pos Tagging ◽  
Word Position

Usually taken as linguistic features by Part-Of-Speech (POS) tagging, Named Entity Recognition (NER) is a major task in Natural Language Processing (NLP). In this paper, we put forward a new comprehensive-embedding, considering three aspects, namely character-embedding, word-embedding, and pos-embedding stitched in the order we give, and thus get their dependencies, based on which we propose a new Character–Word–Position Combined BiLSTM-Attention (CWPC_BiAtt) for the Chinese NER task. Comprehensive-embedding via the Bidirectional Llong Short-Term Memory (BiLSTM) layer can get the connection between the historical and future information, and then employ the attention mechanism to capture the connection between the content of the sentence at the current position and that at any location. Finally, we utilize Conditional Random Field (CRF) to decode the entire tagging sequence. Experiments show that CWPC_BiAtt model we proposed is well qualified for the NER task on Microsoft Research Asia (MSRA) dataset and Weibo NER corpus. A high precision and recall were obtained, which verified the stability of the model. Position-embedding in comprehensive-embedding can compensate for attention-mechanism to provide position information for the disordered sequence, which shows that comprehensive-embedding has completeness. Looking at the entire model, our proposed CWPC_BiAtt has three distinct characteristics: completeness, simplicity, and stability. Our proposed CWPC_BiAtt model achieved the highest F-score, achieving the state-of-the-art performance in the MSRA dataset and Weibo NER corpus.

Current Political News Translation Model Based on Attention Mechanism

2021 ◽  
pp. 94-102
Author(s):  
Xixi Luo ◽  
Jiaqi Yan ◽  
Xinyu Chen ◽  
Yingjiang Wu ◽  
Ke Wu ◽  
...  
Keyword(s):  
Attention Mechanism ◽  
Translation Model ◽  
Political News ◽  
Model Based

scholarly journals “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models

2018 ◽  
Vol 9 (28) ◽  
pp. 6091-6098 ◽  
Author(s):  
Philippe Schwaller ◽  
Théophile Gaudin ◽  
Dávid Lányi ◽  
Costas Bekas ◽  
Teodoro Laino
Keyword(s):  
Organic Chemistry ◽  
Machine Translation ◽  
Chemical Reactions ◽  
Language Processing ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Complex Organic

Using a text-based representation of molecules, chemical reactions are predicted with a neural machine translation model borrowed from language processing.

Japanese translation teaching corpus based on bilingual non parallel data model

2020 ◽  
pp. 1-11
Author(s):  
Zheng Guo ◽  
Zhu Jifeng
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Data Model ◽  
Graph Representation ◽  
Small Scale ◽  
Translation Model ◽  
Parallel Corpus ◽  
Parallel Data ◽  
Intelligent Technology ◽  
Japanese Translation

In recent years, with the development of Internet and intelligent technology, Japanese translation teaching has gradually explored a new teaching mode. Under the guidance of natural language processing and intelligent machine translation, machine translation based on statistical model has gradually become one of the primary auxiliary tools in Japanese translation teaching. In order to solve the problems of small scale, slow speed and incomplete field in the traditional parallel corpus machine translation, this paper constructs a Japanese translation teaching corpus based on the bilingual non parallel data model, and uses this corpus to train Japanese translation teaching machine translation model Moses to get better auxiliary effect. In the process of construction, for non parallel corpus, we use the translation retrieval framework based on word graph representation to extract parallel sentence pairs from the corpus, and then build a translation retrieval model based on Bilingual non parallel data. The experimental results of training Moses translation model with Japanese translation corpus show that the bilingual nonparallel data model constructed in this paper has good translation retrieval performance. Compared with the existing algorithm, the Bleu value extracted in the parallel sentence pair is increased by 2.58. In addition, the retrieval method based on the structure of translation option words graph proposed in this paper is time efficient and has better performance and efficiency in assisting Japanese translation teaching.

Hybridization between Neural Computing and Nature-Inspired Algorithms for a Sentence Similarity Model Based on the Attention Mechanism

2021 ◽  
Vol 20 (1) ◽  
pp. 1-21
Author(s):  
Peiying Zhang ◽  
Xingzhe Huang ◽  
Maozhen Li ◽  
Yu Xue
Keyword(s):  
Neural Networks ◽  
Customer Service ◽  
Language Processing ◽  
Question Answering ◽  
Hybrid Algorithms ◽  
Attention Mechanism ◽  
Similarity Analysis ◽  
Model Based ◽  
Sentence Similarity ◽  
Nature Inspired Algorithms

Sentence similarity analysis has been applied in many fields, such as machine translation, the question answering system, and voice customer service. As a basic task of natural language processing, sentence similarity analysis plays an important role in many fields. The task of sentence similarity analysis is to establish a sentence similarity scoring model through multi-features. In previous work, researchers proposed a variety of models to deal with the calculation of sentence similarity. But these models do not consider the association information of sentence pairs, but only input sentence pairs into the model. In this article, we propose a sentence feature extraction model based on multi-feature attention. In addition, with the development of deep learning and the application of nature-inspired algorithms, researchers have proposed various hybrid algorithms that combine nature-inspired algorithms with neural networks. The hybrid algorithms not only solve the problem of decision-making based on multiple features but also improve the performance of the model. In the model, we use the attention mechanism to extract sentence features and assign weight. Then, the convolutional neural network is used to reduce the dimension of the matrix. In the training process, we integrate the firefly algorithm in the neural networks. The experimental results show that the accuracy of our model is 74.21%.

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