Evaluation of English–Slovak Neural and Statistical Machine Translation

Benková, Lucia; Munková, Daša; Benko, Ľubomír; Munk, Michal

doi:10.3390/app11072948

Cited by 17 publications

(15 citation statements)

References 33 publications

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“…Unfortunately, this method requires a lot of CPU and hardware resources. The outcomes are similarly unpredictable and inconsistent [29]. However, the corpusbased technique compares the translated text to its original text in a parallel corpus or to another corpus built with similar design requirements in the same or another language.…”

Section: Machine Translationmentioning

confidence: 99%

Jahai language repository: a mobile application

Rahman

Mohamad

Mohtar

et al. 2023

IJEECS

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The Jahai language is facing extinction primarily because it is among the least spoken language among minority groups in Malaysia. This is due to lesser speakers and the shift to using a more dominant language. The Jahai tribe is one of the community groups living in the Royal Belum Perak State Park. People who need assistance from Jahai people often face difficulties in communicating with them due to the language barrier. Therefore, a mobile translation system was developed to preserve the language. The system translates the Jahai terms into Malay language. This way, by using the system, other ethnics in Malaysia can understand the language especially when communicating with Jahai people. Three main steps are required in the translation process; first, key in the text input via special character keypad. Then, the system will search the matching word in the database. Finally, the meaning of the word will be displayed. The testing results have indicated this system is functional and accepted with the SUS score of 94/100. Several future recommendations could be made such as including voice search function and adding more Jahai terms in other categories so as to improve the functionality and usability of this proposed system.

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Section: Machine Translationmentioning

confidence: 99%

Jahai language repository: a mobile application

Rahman

Mohamad

Mohtar

et al. 2023

IJEECS

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“…It mainly focuses on the real distribution of data and emphasizes the visualization of data. So the analyst can see the hidden rules in the data at a glance, so as to get inspiration and to help the analyst find a model suitable for the data [10]. e system can not only help MT solve various complex problems in translation, but also provide powerful decision support for MT and realize control optimization [11].…”

Section: Research Status and Development Of English Machine Translati...mentioning

confidence: 99%

Analysis of English Translation Model Based on Artificial Intelligence Attention Mechanism

Lihua

2022

Mathematical Problems in Engineering

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The particularity and quantity of English translation terms have a great impact on the quality and effect of machine translation and can not meet the requirements of English translation of terms. At the same time, technical exchange and communication in different fields need the expression of professional terms. In addition, although the neural machine translation model has good translation performance, it is not ideal for target languages with small translation needs and limited corpus resources. In order to solve the problems existing in the English translation model, this paper constructs the transformer model by replacing the cyclic neural network variables and introducing the attention mechanism. The term information is integrated into two pretraining models to improve the learning ability of model language sentence relationship. Maintain the integrity of terminology information by fully completing the training. The experimental results show that, compared with the other three term translation models, the translation model in this paper has the advantage of term information. At the same time, the deep neural network English term translation model can obtain more fine-grained word relevance. In different corpora, the Bleu score of the model is good, showing obvious translation performance advantages. This study provides a good professional reference value for the translation of English terms.

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“…Metrics of accuracy are based on the closeness of the MT output/hypothesis (h) with the reference (r) in terms of n-grams; they calculate their lexical overlap in (A) the number of common words (h \ r); (B) the length (number of words) of MT output and (C) the length (number of words) of the reference (Benkova et al, 2021). The higher the values of these metrics, the higher the translation quality (Benkova et al, 2021).…”

Section: Automatic Evaluation Metricsmentioning

confidence: 99%

The role of automated evaluation techniques in online professional translator training

Munková

Munk

Benko

et al. 2021

PeerJ Computer Science

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The rapid technologisation of translation has influenced the translation industry’s direction towards machine translation, post-editing, subtitling services and video content translation. Besides, the pandemic situation associated with COVID-19 has rapidly increased the transfer of business and education to the virtual world. This situation has motivated us not only to look for new approaches to online translator training, which requires a different method than learning foreign languages but in particular to look for new approaches to assess translator performance within online educational environments. Translation quality assessment is a key task, as the concept of quality is closely linked to the concept of optimization. Automatic metrics are very good indicators of quality, but they do not provide sufficient and detailed linguistic information about translations or post-edited machine translations. However, using their residuals, we can identify the segments with the largest distances between the post-edited machine translations and machine translations, which allow us to focus on a more detailed textual analysis of suspicious segments. We introduce a unique online teaching and learning system, which is specifically “tailored” for online translators’ training and subsequently we focus on a new approach to assess translators’ competences using evaluation techniques—the metrics of automatic evaluation and their residuals. We show that the residuals of the metrics of accuracy (BLEU_n) and error rate (PER, WER, TER, CDER, and HTER) for machine translation post-editing are valid for translator assessment. Using the residuals of the metrics of accuracy and error rate, we can identify errors in post-editing (critical, major, and minor) and subsequently utilize them in more detailed linguistic analysis.

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Evaluation of English–Slovak Neural and Statistical Machine Translation

Cited by 17 publications

References 33 publications

Jahai language repository: a mobile application

Jahai language repository: a mobile application

Analysis of English Translation Model Based on Artificial Intelligence Attention Mechanism

The role of automated evaluation techniques in online professional translator training

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