A machine learning approach to the identification of translational language : an inquiry into translationese learning models
In the world of Descriptive Translation Studies, translationese refers to the specific traits that characterise the language used in translations. While translationese has been often investigated to illustrate that translational language is different from non-translational language, scholars have also proposed a set of hypotheses which may characterise such di erences. In the quest for the validation of these hypotheses, embracing corpus-based techniques had a well-known impact in the domain, leading to several advances in the past twenty years. Despite extensive research, however, there are no universally recognised characteristics of translational language, nor universally recognised patterns likely to occur within translational language. This thesis addresses these issues, with a less used approach in the eld of Descriptive Translation Studies, by investigating the nature of translational language from a machine learning perspective. While the main focus is on analysing translationese, this thesis investigates two related sub-hypotheses: simplication and explicitation. To this end, a multilingual learning framework is designed and implemented for the identification of translational language. The framework
Ilisei, Iustina-Narcisa
Translationese, Simplification Universal, Explicitation Universal, Translation Universals, Third Code ; nature of translated language, Machine Learning, Multilingual Methodology, Translation Studies ; Translation Theory, Natural Language Processing, Computational Linguistics, Romanian Translational Corpus ; Corpora, Spanish, Romanian, translations, texts, text
A machine learning approach to the identification of translational language : an inquiry into translationese learning models. In the world of Descriptive Translation Studies, translationese refers to the specific traits that characterise the language used in translations. While translationese has been often investigated to illustrate that translational language is different from non-translational language, scholars have also proposed a set of hypotheses which may characterise such di erences. In the quest for the validation of these hypotheses, embracing corpus-based techniques had a well-known impact in the domain, leading to several advances in the past twenty years. Despite extensive research, however, there are no universally recognised characteristics of translational language, nor universally recognised patterns likely to occur within translational language. This thesis addresses these issues, with a less used approach in the eld of Descriptive Translation Studies, by investigating the nature of translational language from a machine learning perspective. While the main focus is on analysing translationese, this thesis investigates two related sub-hypotheses: simplication and explicitation. To this end, a multilingual learning framework is designed and implemented for the identification of translational language. The framework
machine_learn identification translational language inquiry learning model world descriptive_translation_studies refers specific trait characterise language_use translation often investigated illustrate translational language different non translational language scholar also_propose set hypothesis may characterise quest validation hypothesis embrace corpus_based technique well_known impact domain lead several advance past despite extensive however universally recognise characteristic translational language universally recognise pattern likely occur_within translational language address_issue less used descriptive_translation_studies investigate_nature translational language machine_learn perspective main_focus analyse investigate two_related sub hypotheses explicitation end multilingual learning framework_design implemented identification translational language framework