Discriminative ridge regression algorithm for adaptation in statistical machine translation

Chinea-Rios, Mara; Sanchis-Trilles, Germán; Casacuberta, Francisco

doi:10.1007/s10044-018-0720-5

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…can provide comparable translation quality and reduce computing costs compared to estimation methods with prior art. In addition, the experimental results were consistent between different corpora and language pairs [12]. Kazemi et al (2017) proposed a reordering model (R.M.)…”

Section: Literature Reviewsupporting

confidence: 63%

Application of Data Mining Methods in Internet of Things Technology for the Translation Systems in Traditional Ethnic Books

Luo

Xiang

2020

IEEE Access

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In order to translate the ethnic classics, based on the research on the Internet of things, machine learning, and translation technology of ethnic classics, the log-linear model is combined with the national corpus scale and the grammatical structure characteristics, and the phrase statistical machine translation is used to establish a discontinuous phrase extraction model. Then, the translation technology is studied from the three aspects of model definition, training, and decoding. Finally, the algorithm is compared with the traditional phrase extraction algorithm to verify its effectiveness. The results show that the extraction number of discontinuous phrase extraction model is significantly higher than that of traditional phrase extraction model, and the model can extract more phrases, handle larger and more complex text, and score higher in translation fluency. From the evaluation indexes scores of Bilingual Evaluation Understudy (B.L.E.U.) and National Institute of Standards and Technology (N.I.S.T.), it can be found that the B.L.E.U. and N.I.S.T. values of the traditional phrase extraction algorithm are lower than those of the discontinuous phrase extraction model algorithm. The discontinuous phrase extraction algorithm can not only extract the regular continuous phrase, but also obtain the discontinuous text, and the translation effect is better. In conclusion, the combination of Internet of things and machine learning can be used in the translation of ethnic classics to achieve high-quality translation of discontinuous phrases, which is of guiding significance for the study of machine translation.

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Section: Literature Reviewsupporting

confidence: 63%

Application of Data Mining Methods in Internet of Things Technology for the Translation Systems in Traditional Ethnic Books

Luo

Xiang

2020

IEEE Access

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“…In 2017, Chinea-Rios et al [16] proposed the discriminative ridge regression algorithm. This method uses the N-best hypothesis list given by all hypotheses to configure a weight vector so that each sentence is evaluated by professional translators after the output of the editing system.…”

Section: Related Workmentioning

confidence: 99%

Improving Language Translation Using the Hidden Markov Model

Chang¹,

Xiao-liang²,

Xue³

et al. 2021

Computers, Materials &Amp; Continua

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Translation software has become an important tool for communication between different languages. People's requirements for translation are higher and higher, mainly reflected in people's desire for barrier free cultural exchange. With a large corpus, the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units. Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation, ignoring context. To support the ongoing improvement of translation methods built upon deep learning, we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation. During translation, our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence. The collection of all of the generated sentences forms a topic sequence. Using probabilities and article sequences determined from the training set, our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation. This algorithm improves the accuracy of translation, avoids the combination of invalid words, and enhances the readability and meaning of the resulting translation.

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Design of Networked Intelligent Translation System Based on Machine Learning Algorithm

Hong

2021

J. Phys.: Conf. Ser.

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China’s social and economic development is relatively rapid, and China’s opening to the outside world is further expanding. Under this background, the demand for foreign language professionals is also expanding. This paper designs a networked intelligent translation system based on machine learning algorithm, and proposes a cross-language information extraction method based on bilingual word representation. The learning of bilingual word representation can be divided into two stages: unsupervised and supervised. Capturing Chinese and English bilingual semantic information. The cross-language information extraction method based on machine learning algorithm reduces the impact of translation errors and language gaps on cross-language classification performance, obtains effective bilingual semantic information and annotation information, and improves the cross-language information extraction performance. The test results show that the designed automatic English translation system can effectively and quickly realize intelligent English translation of memory-assisted long characters, with high data recall rate, good accuracy and reliability, and good system compatibility, which is worthy of popularization and practical application.

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Discriminative ridge regression algorithm for adaptation in statistical machine translation

Cited by 5 publications

References 16 publications

Application of Data Mining Methods in Internet of Things Technology for the Translation Systems in Traditional Ethnic Books

Application of Data Mining Methods in Internet of Things Technology for the Translation Systems in Traditional Ethnic Books

Improving Language Translation Using the Hidden Markov Model

Design of Networked Intelligent Translation System Based on Machine Learning Algorithm

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