Neural Machine Translation With GRU-Gated Attention Model

Zhang, Biao; Xiong, Deyi; Xie, Jun; Su, Jinsong

doi:10.1109/tnnls.2019.2957276

Cited by 93 publications

(43 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the one hand, the similarity of English words can help AI quickly understand a kind of similar text information, but on the other hand, if there is a large error in the recognition of English word similarity, it will also affect the recognition of English words and text by AI [3]. is task is also widely used in machine translation [4]. In addition, nonnative language learners also need to understand and learn similar English words when learning English, which needs the help of an English dictionary [5].…”

Section: Introductionmentioning

confidence: 99%

Accurate Estimation of English Word Similarity Based on Semantic Network

Gong

2021

Security and Communication Networks

View full text Add to dashboard Cite

The application of artificial intelligence in the field of English needs to process a large amount of English text data, but the deviation of English word similarity reduces its overall English translation accuracy and data processing efficiency. Therefore, this paper proposes an accurate estimation of English word similarity based on semantic network, which combines a variety of computing methods to form a compound computing structure based on semantic network. The experimental results show that the error between the Semantic Web-based English word similarity calculation method and manual evaluation is small, and the accuracy of English word similarity calculation is improved to a certain extent. In addition, compared with other English word similarity calculation methods, the English word similarity calculation method based on semantic network is more in line with people’s cognition and understanding of knowledge, has higher reliability, and has certain practical value in the field of English.

show abstract

Section: Introductionmentioning

confidence: 99%

Accurate Estimation of English Word Similarity Based on Semantic Network

Gong

2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…We employed the encoder structure of the seq2seq model [24] here as the instance feature extractor. The embedding layer [25] was employed to represent bases (15 (A, T, G, C, N, H, B, D, V, R, M, S, W, Y, K) → 4 (representative dimension)) ∘ The encoder used a bi-directional RNN structure, which given equal attention to the head and the tail of the instance, and the output was a context vector [26] to represent the feature of the instance. And subsequently, through the MIL layer, the features of all instances were scored and aggregated jointly to determine the type of the bag [20, 21, 27] .…”

Section: Methodsmentioning

confidence: 99%

Attention-based deep multiple instances learning for classifying circular RNA and other long non-coding RNA

Liu

Peng

et al. 2021

Preprint

View full text Add to dashboard Cite

Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture, which can be fed with raw sequence, to learn the sparse features in sequences and accomplish the identification task for circRNAs. The model outperformed previously reported models. Following the effectiveness validation of the attention score by the handwritten digit dataset, the key sequence loci underlying circRNAs recognition were obtained based on the corresponding attention score. Moreover, the motif enrichment analysis of the extracted key sequences identified some of the key motifs for circRNA formation. In conclusion, we designed a deep learning network architecture suitable for gene sequence learning with sparse features and implemented to the circRNA identification, and the network has a strong representation capability with its indication of some key loci.

show abstract

“…With the development of neural networks, the birth of neural machine translation technology has opened up a new path in the field of machine translation [4]. e emergence of neural machine translation technology has solved many of the abovementioned shortcomings of statistical machine translation.…”

Section: Related Workmentioning

confidence: 99%

Translation Mechanism of Neural Machine Algorithm for Online English Resources

Yanping

2021

Complexity

View full text Add to dashboard Cite

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.

show abstract

Neural Machine Translation With GRU-Gated Attention Model

Cited by 93 publications

References 23 publications

Accurate Estimation of English Word Similarity Based on Semantic Network

Accurate Estimation of English Word Similarity Based on Semantic Network

Attention-based deep multiple instances learning for classifying circular RNA and other long non-coding RNA

Translation Mechanism of Neural Machine Algorithm for Online English Resources

Contact Info

Product

Resources

About