RNN based machine translation and transliteration for Twitter data

Vathsala, M. K.; Holi, Ganga

doi:10.1007/s10772-020-09724-9

Cited by 40 publications

(13 citation statements)

References 11 publications

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“…For this reason, Wei et al proposed long short-term memory (LSTM). is network retains the basic model of RNN and replaces the normal nodes (usually tanh) in RNN with long short-term memory nodes to make it have the sequence processing capability of RNN while improving the gradient vanishing problem, so it is widely used in sequence data processing tasks [25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

Cang

Huang

2021

Computational Intelligence and Neuroscience

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Musical choreography is usually completed by professional choreographers, which is very professional and time-consuming. In order to realize the intelligent choreography of musical, based on the mixed density network (MDN), this paper generates the dance matching with the target music through three steps: motion generation, motion screening, and feature matching. The choreography results in this paper have a high degree of matching with music, which makes it possible for the development of motion capture technology and artificial intelligence and computer automatic choreography based on music. In the process of motion generation, the average value of Gaussian model output by MDN is used as the bone position and the consistency of motion is measured according to the change rate of joint velocity in adjacent frames in the process of motion selection. Compared with the existing studies, the dance generated in this paper has improved in motion coherence and realism. In this paper, a multilevel music and action feature matching algorithm combining global feature matching and local feature matching is proposed. The algorithm improves the unity and coherence of music and action. The algorithm proposed in this paper improves the consistency and novelty of movement, the compatibility with music, and the controllability of dance characteristics. Therefore, the algorithm in this paper technically changes the way of artistic creation and provides the possibility for the development of motion capture technology and artificial intelligence.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

Cang

Huang

2021

Computational Intelligence and Neuroscience

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“…where D is the start symbol cluster, W E is the element in F O , W E is the cyclic symbol cluster, W V is the termination symbol cluster, and α is the phrase action cluster [20].…”

Section: Corpus Part Of Speech Recognition Based On Intelligentmentioning

confidence: 99%

English Translation Model Based on Intelligent Recognition and Deep Learning

2022

Wireless Communications and Mobile Computing

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Aiming at the problems of low accuracy of English phrase part of speech recognition, poor English translation effect, and long translation time in the traditional English translation model, an English translation model based on intelligent recognition and deep learning is designed. An English phrase corpus was built, the phrase antecedent and postscript likelihood of the improved GLR algorithm by using the quaternion cluster were calculated, and the part of speech of the English phrase corpus was identified. According to the recognition results, the feature extraction algorithm is introduced to extract the best contextual features. On this basis, a neural machine translation model is constructed by integrating the traditional neural network in deep learning and combining the attention mechanism. It is used as a neural machine translation model for English translation. The simulation results show that the English translation model based on intelligent recognition and deep learning has high phrase recognition accuracy, good translation effect, and short translation time, which improves the quality of English translation.

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“…Transliteration algorithms [43], [44] and word dictionaries [45], [46] are used to convert the Roman form of Urdu to Urdu script but accuracy is greatly reduced because of the presence of English words with Roman Urdu in conversational use. Soundex algorithm [47] is used to solve conversational issues that assigns the code to English words on the bases of the position that are then mapped to Urdu script [48].…”

Section: Related Workmentioning

confidence: 99%

RUTUT: Roman Urdu to Urdu Translator Based on Character Substitution Rules and Unicode Mapping

et al. 2020

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Urdu language written in English alphabets for communication is known as Roman Urdu. In pronunciation, both are the same but different in spelling and have different shapes of the alphabet. A survey acknowledges that 300 million people are speaking Urdu and about 11 million speakers in Pakistan from which maximum users prefer Roman Urdu for the textual communication. Today most of the modern technologies like computers and mobile phones using English script, due to this local Urdu user has to use English letters to type Urdu script that is Roman Urdu. In this research, Roman Urdu to Urdu Translator (RUTUT) is proposed that consists of preprocessing methods, rule-based character substitution and Unicode based character mapping techniques. It can transliterate the messages or descriptions from the Roman Urdu script to Urdu script which may help the Urdu speaker to elaborate their message in efficient manners. The focus of this research is to analyze the issues related to the Roman Urdu script to Urdu script transliteration and develop a translator based on the concepts of transliteration. This research analyzed Roman Urdu data and identified different rules-based character substitution techniques that transform the Roman Urdu into Urdu script at fundamental levels. This research is carried out using a python programming language in programming tool Anaconda in Jupiter notebook and user-friendly Graphical User Interface (GUI) created by using Tkinter library. To evaluate the RUTUT, different translational tests are performed and compare those results with famous Google online translator and ijunoon online transliteration. The analyses of results show that the proposed RUTUT approach translates accurately than Google online translator and ijunoon online transliteration.

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RNN based machine translation and transliteration for Twitter data

Cited by 40 publications

References 11 publications

[Retracted] Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

[Retracted] Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

English Translation Model Based on Intelligent Recognition and Deep Learning

RUTUT: Roman Urdu to Urdu Translator Based on Character Substitution Rules and Unicode Mapping

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