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EN
People from war-torn countries and countries suffering economic hardships are on the move in search of a better life or of a refuge. The host countries are challenged: To ensure equal access to public services, civil and political participation of migrants and refugees means to tear down the language barrier. Only translating and interpreting can offer a realistic, time-sensitive solution to this challenge and give “speechless” communities a voice. It is the objective of the article to investigate the expectations we can have from technology in public service translation compared to the performance of professional communicators; The issue of what a machine can(not) do and to fathom, when a professional translator has to intervene, needs a bottom-up approach and will be discussed against the backdrop of public service translation as domain-specific intercultural communication.
EN
The article concerns the problem of machine translation, discussing both its advantages and drawbacks. The author’s main purpose is to put the popular online translator, Google Translate, to the test by means of various text types. Google translations are then analyzed and compared to their source texts. This will allow one to specify with which text types Google can cope considerably well, and which it cannot handle at a satisfactory level. The analysis will also shed some light on the future role of human translators.
EN
The paper presents the preliminary results of an object-oriented disambiguation of the French verb aller 'to go', performed for the purpose of machine translation. The Orst part introduces the theoretical foundations of the object-oriented method proposed by Wiesław Banyś, and the principles of word sense disambiguation. The second part is an analysis of the diQerent uses of the verb aller accompanied by temporal markers (weekdays, speciOc temporal markers and frequency markers). By using concrete examples, the author tries to discover the linguistic conditions which determine whether the translation of the analyzed verb should be jechać (the semelfactive form) or jeździć (the iterative form). The results of the research are presented in four syntactic-semantic schemes, i.e. in one of the descriptive formats used in the object-oriented approach.
EN
The presented paper focuses on the problem of machine translation, especially statistical machine translation. The idea behind MT was the concept of the computers’ ability to translate some basic and schematical texts by using databases of grammar and vocabulary. Currently MT is not only a fast-developing field of study, but also a popular and (in many cases) free of charge method of translating texts for personal use, for example via Google Translate.
EN
The aim of this article is to investigate the usefulness and applicability of CAT (Computer-Aided Translation) programmes in relation to the qualities (e.g. standardisation, predictability, terminology) of the translated text. In the study both scientific articles and translator’s forums are taken into account in order to establish advantages and limitations of commercial CATs. It appears that CAT programmes influence cognitively the translator’s work and even though they are supposed to facilitate his or her work, they may hinder or slow down the process of translation. These programmes are also applicable only in the case of certain types of texts, namely those which are standard and predictable and they fail in the case of texts which are linguistically or culturally-coloured. Furthermore, translators express numerous practical concerns regarding CATs (e.g. their price, instability). However, their use has become a very basic translation skill and it is no longer an advantage but an absolute necessity for anyone wishing to work as a translator.
Studia Rossica Posnaniensia
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2019
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vol. 44
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issue 2
313-324
PL
Machine translation (MT) is a relatively new field of science. MT systems are evolving in certain directions. The article discusses the possibilities and the future of systems currently offered to public by the biggest technological companies focusing on English-Russian translation relations.
EN
Our goal is to follow and analyse the development of mathematical linguistics in Russia and in the USSR. The first application of mathematical methods, primarily statistical, occurred in Russia already in mid-19th century; however a true bloom of mathematical linguistics happens in mid-20th century, owing to the initiation of intensive research into machine translation. Both the rapid development of machine translation in the initial phase, and later curtailment of research due to considerable financial limitations, has a political-historical justification. We finish our description of the history of linguistic research with the use of mathematical methods with the moment of the collapse of the USSR. Additionally, we show the evolution of the methodology of scientific research within mathematical linguistics.
EN
Cognate status is one of the most complicated issues for those who deal with or are interested in linguistics. In the present study, we have provided a general overview related to this specific matter, and compiled a list of English-Turkish cognates and false cognates. According to the derived list, we determined that 2411 of English words, examined from among approximately 80,000 words, are either cognates or false cognates in Turkish. After determining the number of cognate and false cognate words, we tested and evaluated the correctness of the translations of three software programs and five websites that provide translation services using some of the cognates and false cognates from the derived list. Results suggest that cognate words are translated correctly in most sentences at lexical level, while false cognates and especially partial false cognates are mostly translated wrongly. Nevertheless, at sentential level, it is revealed that almost all sentences translated by computer are unsatisfactory, and need human correction.
EN
Machine translation systems as an aid for German language translators The aim of the article is to evaluate the usefulness of two machine translation systems, Google Translate and DeepL Translator, to German language translators. Both specialist texts and standard ones have been analysed in terms of translation errors and linguistic defects that required human intervention in the machine translations. A discussion of advantages and disadvantages of the systems has resulted in their general assessment as an aid in the translation process.
EN
The article presents an analysis of selected olfactory perception nouns in French for the purpose of machine translation. The initial hypothesis, according to which nouns such as odeur, parfum, arôme, puanteur, senteur create a coherent set, i.e. an object class characterized by a certain group of operations (verbs), is subjected to corpus verification. The research, based on the French corpus frTenTen12, confirms this hypothesis and allows to distinguish 100 verbal operators common to all the elements of the studied class. In the further part of the article, examples of descriptions of the collected language material are presented in IT-implementable formats, which can be used in machine translation software. The first table shows the syntactic combinatorics of the class in French and Polish and the second one takes the form of bilingual lexicographical “flashcard”, in which the operators characterizing the studied class are divided into three groups: constructors, manipulators and accessors, according to the object-oriented approach by Wiesław Banyś.
PL
W artykule przedstawiona jest analiza wybranych francuskich rzeczowników percepcji węchowej dla celów tłumaczenia automatycznego. Weryfikacji korpusowej poddana została hipoteza wyjściowa, zgodnie z którą rzeczowniki takie jak odeur, parfum, arôme, puanteur, senteur tworzą semantyczno-gramatycznie koherentny zbiór tzn. klasę obiektową charakteryzującą się pewnym wspólnym zestawem operacji (czasowników). Badania w oparciu o korpus językowy frTenTen12 pozwalają hipotezę tę potwierdzić i wyróżnić sto operatorów czasownikowych wspólnych dla wszystkich elementów badanej klasy. W dalszej części artykułu zaprezentowane są przykładowe opisy zebranego materiału językowego w formatach informatycznie implementowanych tj. takich, które mogą znaleźć zastosowanie w programach do automatycznego tłumaczenia tekstów. Pierwsza tabela przedstawia kombinatorykę składniową klasy w języku francuskim i polskim; druga natomiast przyjmuje formę dwujęzycznej „fiszki” leksykograficznej, w której operatory charakteryzujące badaną klasę są podzielone, zgodnie z założeniami ujęcia zorientowanego obiektowo W. Banysia, na trzy grupy: konstruktory, manipulatory i akcesory.
EN
The study explores translation quality by analysing two Czech professional translations of English newspaper articles. The original idea was for a tandem of translators-cum-theoreticians to synthesise the best of the two translations while introducing slight to moderate modifications where necessary, to produce an optimal reference translation, i.e., a translation thought to be the best possible that can be achieved by a team of human translators; optimal reference translations can be used in assessments of excellent machine translations. It soon became apparent, however, that a considerable amount of editing and creativity was needed from the team striving for an optimal reference translation, prompting the present authors to subject the original translations to a detailed assessment. The primary focus is on the formal aspect of the translations and the phenomenon known as ‘translationese’, which is understood here to refer to a lack of sensitivity to target language usage. The problems identified fall into a wide range of categories such as spelling, morphosyntax, grammar, lexicon and word formation. Special attention is paid to source-language interference; having reviewed existing theoretical discussions of interference, the authors drafted a typology which was then expanded to include several other types of errors recurrent in the translations analysed.
Porównania
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2020
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vol. 26
|
issue 1
299-313
EN
In 1950, the brilliant British mathematician Alan Turing proposed a test to determinea computer’s ability to generate natural language sentences. The computer passed the test when the human communicating with it by means of a screen was unable to discern if they were talking to another human or to a machine. Today the dynamic development of machine translation software makes us wonder about the possibilities of automatically translating literature, including poetry. Can a computer- generated translation pass for a human one? What linguistic and textual phenomena are most likely to expose the artificial intelligence of the translator? Can the computer-generated translation be viewed as a work of art?
PL
W 1950 roku wybitny brytyjski matematyk Alan Turing zaproponował test określający zdolność komputera do generowania zdań języka naturalnego. Komputer pomyślnie przechodził próbę, jeśli rozmawiający z nim za pośrednictwem ekranu człowiek nie był w stanie stwierdzić, czy jego interlokutorem jest homo sapiens, czy maszyna. Dziś dynamiczny rozwój komputerowych programów tłumaczeniowych skłania do pytań o możliwości maszynowego przekładu tekstu literackiego, w tym poetyckiego. Czy tłumacz elektroniczny może przełożyć wiersz tak, by odbiorca myślał, że przekładu dokonał człowiek? Jakie zjawiska językowe i tekstowe najbardziej demaskują sztuczną inteligencję translatora? Czy stworzony w ten sposób tekst można rozpatrywać w kategorii dzieła sztuki?
Porównania
|
2020
|
vol. 26
|
issue 1
283-297
EN
The more technological development, the greater the participation of the human – in formulating tasks and problems, supervising and improving automated processes and interpreting their outcomes. The hierarchy is preserved, humans are still indispensable, but it does not mean that in certain areas of machinery the potential does not really exceed that of the human and that this advantage is not worth exploiting. Natural language processing (NLP) is not a young field, but in recent years, thanks to the thrive of deep learning methods, data and knowledge mining or new human-machine interfaces, computer text analysis is experiencing a real renaissance. As far as translation is concerned, it is mostly algorithms for machine translation that are being discussed. This article, on the other hand, presents a slightly broader spectrum of the translation process and looks at the accompanying elements (such as criticism) in which the use of NLP methods may bring new and interesting results. Results which, due to limited computing power, humans are unable to achieve. The discussion in the paper covers such aspects as the vector representation of language,stylometry and its application, or the analysis of large data sets – all for the purposes of translation and translatology.
PL
Przewrotna jest rola postępu – im więcej technologicznego rozwoju, tym większy udział człowieka – w koncepcji, formułowaniu zadań, interpretacji wyników, nadzorze i korekcie. Hierarchia jest zachowana, człowiek wciąż nieodzowny, ale to nie znaczy, że w pewnych obszarach maszynowy potencjał rzeczywiście nie przewyższa ludzkiego i że nie warto z tej przewagi skorzystać. Przetwarzanie języka naturalnego (NLP) to dziedzina niemłoda, ale w ostatnich latach dzięki rozkwitowi metod uczenia głębokiego (deep learning), mody na maszynowe wnioskowanie (data/knowledge mining) czy nowym sprzętowym interfejsom (m.in. zaawansowane rozpoznawanie obrazu) komputerowa analiza tekstu przeżywa istny renesans. W odniesieniu do translacji przyjęło się mówić i pisać głównie o coraz doskonalszych lub właśnie zupełnie niemożliwych algorytmach dla kolejnych par języków czy coraz większej precyzji samego tłumaczenia. Niniejszy artykuł przedstawia natomiast nieco szersze spektrum procesu tłumaczenia i przygląda się elementom przekładowi towarzyszącym (jak choćby krytyka), w których wykorzystanie metod NLP możeprzynieść nowe, ciekawe wyniki. Wyniki, których ze względu na ograniczoną moc obliczeniową człowiek nie jest w stanie osiągnąć. Omówione zostały takie aspekty jak wektorowa reprezentacja języka, stylometria i jej zastosowania czy analiza wielkich zbiorów danych – wszystko to na potrzeby szeroko rozumianychtranslacji i translatologii.
EN
The two main topics discussed in this article are machine translation systems and metaphorical constructions translated by such systems. I briefly present the history of machine translation, its evolution and types of the most widespread engines, taking into account their architecture. I describe rule-based, statistical and neural machine translation, taking Translatica PWN, Google Statistical and Google Neural Machine Translation systems respectively as examples. Having discussed the notion of metaphor, I finally analyse examples of metaphorical expressions and idioms translated by the described engines, compare the final outcome and present my conclusions.
16
51%
EN
The deep learning methods of artificial neural networks have seen a significant uptake in recent years, and have succeeded in overcoming and advancing the success of auto-solving tasks in many fields. The field of computational linguistics and its application offshoot, natural language processing, with classic tasks such as morphological tagging, dependency analysis, named entity recognition and machine translation, are no exception to this. This paper provides an overview of recent advances in these tasks related to the Czech language and presents completely new results in the areas of morphological marking and recognition of named entities in Czech, along with a detailed error analysis.
PL
Niniejszy projekt badawczy ma na celu wykazanie czy jakość tłumaczenia maszynowego jest na tyle dobra, by mogło być ono wykorzystywane podczas pracy profesjonalnego tłumacza prawniczego. Podczas badania analizie poddane zostały umowy – teksty użytkowe charakteryzujące się wysoką powtarzalnością wyrażeń, zwrotów i terminów, złożoną składnią oraz nieprzystawalnością terminologiczną (Šarčević 2000, Berezowski 2008). Przyjęta metoda badawcza polegała na nagraniu procesu tłumaczenia przy zastosowaniu narzędzi Google MT oraz Microsoft MT. Badanie umożliwiło wydobycie informacji na temat użyteczności tłumaczenia maszynowego poprzez określenie: (i)                  rodzaju błędów występujących w tekście wygenerowanym przez tłumacza maszynowego, (ii)                 częstotliwości występowania błędów, (iii)               zgodności merytorycznej z treścią oryginału (liczba pominięć oraz zniekształceń), (iv)               czasu poświęconego na edycję tekstu wygenerowanego przez tłumacza maszynowego. Wyniki badania powinny pomóc tłumaczom w podjęciu świadomej decyzji czy chcieliby włączyć tłumaczenie maszynowe do swojego warsztatu pracy.
EN
The aim of this research project is to verify whether machine translation (MT) technology can be utilized in the process of professional translation. The genre to be tested in this study is a legal contract. It is a non-literary text, with a high rate of repeatable phrases, predictable lexis, culture-bound terms and syntactically complex sentences (Šarčević 2000, Berezowski 2008). The subject of this study is MT software available on the market that supports the English-Polish language pair: Google MT and Microsoft MT. During the experiment, the process of post-editing of MT raw output was recorded and then analysed in order to retrieve the following data: (i)                  number of errors in MT raw output, (ii)                 types of errors (syntactic, grammatical, lexical) and their frequency, (iii)               degree of fidelity to the original text (frequency of meaning omissions and meaning distortions),  (iv)               time devoted to the editing process of the MT raw output.The research results should help translators make an informed decision whether they would like to invite MT into their work environment.
EN
This article deals with the use of lexical resources and corpus tools to evaluate, edit, and verify already translated texts. Additionally, it provides a description of a pilot study whose purpose was to describe students’ behaviour in a situation where machine-translated metaphorical phrases should be evaluated and corrected. The analysis focuses on identifying the lexical resources and tools that were most frequently used at every stage of the task. The exercise was conducted during a course on translating technologies for MA students of translation studies.
PL
Niniejszy artykuł stanowi przegląd zasobów leksykalnych oraz narzędzi korpusowych przydatnych do oceny, edycji oraz weryfikacji przetłumaczonych tekstów. Dodatkowo zawiera opis badania pilotażowego, którego celem było opisanie zachowania studentów w sytuacji, gdy należy ocenić oraz dokonać modyfikacji leksykalno-stylistycznej wyrażenia metaforycznego przetłumaczonego maszynowo. Analizie poddano jedynie to, jakich narzędzi oraz zasobów studenci używali i na jakim etapie zadania. Ćwiczenie zostało wykonane w ramach zajęć na studiach magisterskich, kierunek: przekładoznawstwo – technologie tłumaczeniowe.
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