Hybrid Machine Translation Overview

España-Bonet, Cristina; Costa-jussà, Marta R.

doi:10.1007/978-3-319-21311-8_1

Cited by 3 publications

(6 citation statements)

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“…MT has traditionally presented two different approaches: the approach based on rules (RBMT, Rule-Based Machine Translation), and the corpus-based approach (Hutchins, 2007). Nevertheless, the last few years have seen the development of new architectures, giving rise to hybrid approaches and, most recently, neural MT España-Bonet & Costa-jussà, 2016).…”

Section: Evolution Of Mtmentioning

confidence: 99%

“…Corpus-based automatic systems are divided into statistical MT systems (SMT) and example-based systems (EBMT); despite this, both approaches converge on many aspects and isolating the distinctive characteristics of each one is very complicated (Hutchins, 2007). Moreover, the statistical approach was the predominant model until the recent emergence of neural MT systems (Bojar et al, 2015;España-Bonet & Costa-jussà, 2016;Hutchins, 2007). The advantage of these compared to RBMT systems is their solid performance when selecting the lexicon-especially if focusing on a thematic domain-and the little human effort they require in order to be trained automatically (Hutchins, 2007;Koehn, 2010).…”

Section: Evolution Of Mtmentioning

confidence: 99%

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Machine translation systems and quality assessment: a systematic review

Rivera-Trigueros

2021

Lang Resources & Evaluation

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Nowadays, in the globalised context in which we find ourselves, language barriers can still be an obstacle to accessing information. On occasions, it is impossible to satisfy the demand for translation by relying only in human translators, therefore, tools such as Machine Translation (MT) are gaining popularity due to their potential to overcome this problem. Consequently, research in this field is constantly growing and new MT paradigms are emerging. In this paper, a systematic literature review has been carried out in order to identify what MT systems are currently most employed, their architecture, the quality assessment procedures applied to determine how they work, and which of these systems offer the best results. The study is focused on the specialised literature produced by translation experts, linguists, and specialists in related fields that include the English–Spanish language combination. Research findings show that neural MT is the predominant paradigm in the current MT scenario, being Google Translator the most used system. Moreover, most of the analysed works used one type of evaluation—either automatic or human—to assess machine translation and only 22% of the works combined these two types of evaluation. However, more than a half of the works included error classification and analysis, an essential aspect for identifying flaws and improving the performance of MT systems.

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Section: Evolution Of Mtmentioning

confidence: 99%

Section: Evolution Of Mtmentioning

confidence: 99%

Machine translation systems and quality assessment: a systematic review

Rivera-Trigueros

2021

Lang Resources & Evaluation

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“…Target language, TL). Kako je broj fraza koje se mogu izvući iz korpusa veći od broja sintaktičkih elemenata, omogućena je veća sloboda u prijevodu, ali također i veći broj gramatički neispravnih prijevoda (España-Bonet, Costa-jussà, 2016).…”

Section: Statistički Pristup Kod Strojnog Prevođenjaunclassified

“…Hijerarhijski sustavi temeljeni na frazama (HPBSMT) svojevrsna su ravnoteža između pristupa PBSMT i SSMT (Chiang, 2005). Hijerarhijska se fraza sastoji od hijerarhije riječi i podfraza koja je namijenjena uspostavljanju pravilna redoslijeda među frazama (España-Bonet, Costa-jussà, 2016).…”

Section: Statistički Pristup Kod Strojnog Prevođenjaunclassified

“…Kad su hibridni sustavi vođeni SMT sustavima, integracija pravila izvršena je od faze prededitiranja/ posteditiranja do središnjega dijela sustava. Kad su hibridni sustavi vođeni RBMT sustavima, integracija statistike upotrebljava se kako bi se obogatili resursi, moduli temeljeni na podatcima ili okosnica sustava temeljenog na pravilima (España-Bonet, Costa-jussà, 2016).…”

Section: Hibridni Pristup Kod Strojnog Prevođenjaunclassified

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Pregled metoda obrade prirodnih jezika i strojnog prevođenja

Šuman

2021

Zb. Veleuč. Rij. (Online)

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U radu je dan pregled područja povezanog s procesiranjem prirodnih jezika i njihova međusobnog odnosa, počevši od šire domene kao što je umjetna inteligencija, putem strojnog učenja, računalne lingvistike, metoda strojnog prevođenja te posebice onih zasnovanim na dubokom učenju. Opisane su karakteristike, primjene, faze i glavni problemi obrade prirodnih jezika s leksičke, sintaktičke, semantičke, govorne i pragmatičke perspektive. Opisane su faze prepoznavanja i analize prirodnog jezika kao i faza generiranja prirodnih jezika. Postupci pre-editinga i post-editinga uz korištenje kontroliranih prirodnih jezika dani su kao primjeri prakse kojom se povećava točnost i kvaliteta automatskog prevođenja i općenito procesiranja teksta. Poseban je fokus stavljen na strojno prevođenje te metode strojnog prevođenja. Pristupi strojnom prevođenju kao statistički, temeljen na pravilima, hibridni i pristup temeljen na dubokom učenju opisani su i predstavljeni s obzirom na njihove prednosti i nedostatke i prikladnu primjenu u praksi. Na kraju su dani još uvijek neriješeni izazovi kao smjer daljnjih istraživanja vezanih uz obradu prirodnih jezika te značaj razvoja pristupa temeljenog na dubokom učenju.

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An OOV-Aware Curation Process for Psycholinguistic Analysis of Social Media Text - A Hybrid Approach

Liu

2020

Designing for Digital Transformation. Co-Creating Services With Citizens and Industry

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Hybrid Machine Translation Overview

Cited by 3 publications

References 32 publications

Machine translation systems and quality assessment: a systematic review

Machine translation systems and quality assessment: a systematic review

Pregled metoda obrade prirodnih jezika i strojnog prevođenja

An OOV-Aware Curation Process for Psycholinguistic Analysis of Social Media Text - A Hybrid Approach

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