A New Method for Word Alignment of Tibetan-Chinese Machine Translation

2014 ◽  
Vol 1048 ◽  
pp. 521-525
Author(s):  
Fu Cheng Wan ◽  
Xiang Zhen He ◽  
Hong Zhi Yu
Keyword(s):  
Machine Translation ◽  
New Method ◽  
Word Alignment ◽  
Translation Model ◽  
Syntactic Cues

We propose a new simple but effective method for building Tibetan-Chinese machine Translation corpus and a novel Tibetan-Chinese Machine Translation model integrating Tibetan syntactic cues which is based on the Treebank, this model can be used on the system of Tibetan-Chinese Machine Translation successfully . Keywords: syntactic Treebank; Tibetan syntactic cues ; Machine Translation;

Does GIZA++ Make Search Errors?

2010 ◽  
Vol 36 (3) ◽  
pp. 295-302 ◽  
Author(s):  
Sujith Ravi ◽  
Kevin Knight
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Word Alignment ◽  
Alignment Algorithm

Word alignment is a critical procedure within statistical machine translation (SMT). Brown et al. (1993) have provided the most popular word alignment algorithm to date, one that has been implemented in the GIZA (Al-Onaizan et al., 1999) and GIZA++ (Och and Ney 2003) software and adopted by nearly every SMT project. In this article, we investigate whether this algorithm makes search errors when it computes Viterbi alignments, that is, whether it returns alignments that are sub-optimal according to a trained model.

A Topic-Triggered Translation Model for Statistical Machine Translation

2017 ◽  
Vol 26 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Jinsong Su ◽  
Zhihao Wang ◽  
Qingqiang Wu ◽  
Junfeng Yao ◽  
Fei Long ◽  
...  
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation Model

scholarly journals The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0240663
Author(s):  
Beibei Ren
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Machine Translation ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Translation Model ◽  
Translation Quality ◽  
Long Short Term Memory ◽  
Deep Learning Neural Network

With the rapid development of big data and deep learning, breakthroughs have been made in phonetic and textual research, the two fundamental attributes of language. Language is an essential medium of information exchange in teaching activity. The aim is to promote the transformation of the training mode and content of translation major and the application of the translation service industry in various fields. Based on previous research, the SCN-LSTM (Skip Convolutional Network and Long Short Term Memory) translation model of deep learning neural network is constructed by learning and training the real dataset and the public PTB (Penn Treebank Dataset). The feasibility of the model’s performance, translation quality, and adaptability in practical teaching is analyzed to provide a theoretical basis for the research and application of the SCN-LSTM translation model in English teaching. The results show that the capability of the neural network for translation teaching is nearly one times higher than that of the traditional N-tuple translation model, and the fusion model performs much better than the single model, translation quality, and teaching effect. To be specific, the accuracy of the SCN-LSTM translation model based on deep learning neural network is 95.21%, the degree of translation confusion is reduced by 39.21% compared with that of the LSTM (Long Short Term Memory) model, and the adaptability is 0.4 times that of the N-tuple model. With the highest level of satisfaction in practical teaching evaluation, the SCN-LSTM translation model has achieved a favorable effect on the translation teaching of the English major. In summary, the performance and quality of the translation model are improved significantly by learning the language characteristics in translations by teachers and students, providing ideas for applying machine translation in professional translation teaching.

Prospects for the teaching of translation majors in the new era

Babel ◽  
2020 ◽  
Vol 66 (4-5) ◽  
pp. 867-881
Author(s):  
Yanlin Guo
Keyword(s):  
Machine Translation ◽  
Rapid Development ◽  
Professional Knowledge ◽  
Widespread Application ◽  
New Era ◽  
Translation Model ◽  
Translation Errors

Abstract Since entering the new era, the translation model has gradually changed with the widespread application of machine translation technology and the rapid development of a translation industry. The mismatch between the demand of employers and the talents trained by universities has become a major problem facing the translation major nowadays. To this end, we should attach more importance to the readjustment of the existent curriculum; students’ practical ability in translation; grasp of the skill of detecting and correcting machine translation errors; combination of translation and relevant professional knowledge.

A hybrid word alignment approach to build bilingual lexicons for English-Arabic machine translation

2012 ◽  
pp. CSP28
Author(s):  
Nasredine Semmar ◽  
Dhouha Bouamor ◽  
Ali Mohamed Jaoua ◽  
Samir Elloumi ◽  
Fethi Kilani Ferjani ◽  
...  
Keyword(s):  
Machine Translation ◽  
Word Alignment

scholarly journals Bayesian Word Alignment and Phrase Table Training for Statistical Machine Translation

2013 ◽  
Vol E96.D (7) ◽  
pp. 1536-1543
Author(s):  
Zezhong LI ◽  
Hideto IKEDA ◽  
Junichi FUKUMOTO
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Word Alignment
Sign in / Sign up

Export Citation Format

Share Document