The study includes the translation of the article entitled What to Expect from Neural Machine Translation by Joss Moorkens. It aims to eliminate the negative perceptions of translators about machine translations when translating the article. At the same time, attempts were made to show that machine translation actually helps human translators.
I would like to highlight the people who supported and helped me so much during my studies at İstanbul 29 Mayis University. In addition, I am truly grateful to all the teachers who shed light on the knowledge of translation skills, clarified most things in the translation industry, and gave recommendations in the process.
INTRODUCTION
As technology empowered over centuries, it entered our lives and we, as humans, used it in every field like healthcare, service industry, education. Technologies such as CAT tools, terminology databases, translation memories are used in the translation industry. Machine translation is included in these technologies, but there is a perception that it is a threat to translators.
By applying some situations and some theoretical information, I will evaluate the accuracy and falsity of this perception.
AIMS OF THE PROJECT
Machine translation cannot understand the purpose of the text, what to translate for, who to translate for, etc. As we can see in Skopos theory, we need to know the purpose of translation before we start translating a text. This is why machine translation does not have the capacity to translate literary texts, advertising and marketing texts, in fact all text that includes creativity.
I believe that machine translation technology is not a danger, but an advantage to help translators and get new positions. The result of machine translation should not be expected to the same standard as a text translated by a human translator.
THEORETICAL FRAMEWORK
Toury also states that “they allow additional information and a division of labor between the different fields of translation studies” (Toury 1995).
COMMENTARY
Pre-translation Stage
- Subject of the Article
- Terminology
- CAT Tools for Translation
- Process of Uploading the Source Text
Exact title: What to Expect from Neural Machine Translation, Practical Lectures. That year I did a presentation on machine translation and the types of machine translation, basically statistical machine translation, rule-based machine translation, neural machine translation. The article conducts an exercise with translation department students to compare statistical MT and neural MT.
In short, it is the selection of appropriate terms in the target text for terms used in the source text. Because, in my opinion, it is more practical to use and we as a class have generally used Smartcat in lectures.
Translation Stage
- Problems and Decisions
- i CAT tool
- ii The use of words
- iii Grammar
- iv Spelling
- v Others
There are actually many websites used for this purpose, but I wanted to use this website because of its capabilities. Post-editing", for example, confused my mind as to whether to use its Turkish equivalent or just leave it like that. Because everyone knows what post-editing is in the translation sector, and the person reading this article is probably someone who is interested in translation or language.
I have translated it as "control quality device", but I have left the abbreviation as it is (TQA), because (ÇKK) does not mean something for the receiver. In the article, the students had used some sentences to translate it with SMT and NMT. Because "Wikipedia" is the Turkish version of the original page (there are translations and original Turkish content), but the sentences that are the subject of the research are taken from the English page.
There was also a clause like "led to an increase in NMT hype" and I was having trouble finding a word for hype. Moreover, there was also a word that is "effort" and I first translated it as "iş yükti", but then I wanted to change it to "çaba". In the translation process, the translator must be careful about everything such as punctuation etc.
In the article, a post-editing exercise has been done with the students, who are from different countries. Also, when one of the students explains the output of the neural machine translation, she/he has said that "the word 'moistening' was used instead of 'moisture' in the neural machine translation." I first translated them as "nem yerin ıslaklık", but my supervisor suggested that they remain the same. Because we may need to use it for hyphenation, and such an error is not mentioned in the source text, it has.
Post-translation Stage
- Editing
- Proofreading
- QA Report
I exported the translated text as a Word form because it would be easier for me instead of Smartcat. I mean, I had a lot of trouble converting the file first, and then it was about the file format. After realizing those problems, I checked the paper order and comprehensive physical features of my translation project.
I also identified instances such as repeated phrases or punctuation, examined row spacing and notches, and evaluated the overall appearance of the document. Although the text has been edited, there may be features of different cultures in the language, such as repetitions, and this should be reported with the QA report. First of all, as I learned from the lecture on translation technologies, I exported the TMX file of the translation from Smartcat.
I reviewed these results and compared them with the translated file (if it contains any flaws) in Word. Errors in the QA file were generally related to numerical mismatches, but were not errors.
CONCLUSION
Source Text
The second metric uses error marking using a simple typology of errors, common among research and industry models, but little used in academic scenarios.4 The third is post-editing effort, using one of the three effort categories presented. from Krings (2001): time, technical and cognitive effort. How much of the meaning expressed in the source passage appears in the translation passage. One student wrote that 'The neural translation tool proved to be more effective, however it was certainly not perfect and contained a few errors which I spent 20 minutes post-editing time correcting.' Another student found mistranslations via both MT engines, for example when 'the word "moisture" was used instead of "moisture" in the neural machine translation.' "Some words in both [paradigms] were also made plural when there was no reason to do so." Overall, students were surprised by the high quality. of NMT output: 'I've found that NMT produces surprisingly good results in the case of long, multi-clause sentences (which are known to cause a lot of trouble in machine translation in general).' One student suggested that the NMT output was of the highest quality 'because of the multiple operations it is able to perform simultaneously.'
Nevertheless, most students rated the technology positively and would be interested in working with NMT in the future. Incorporating discrete translation lexicons into neural machine translation.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing 2016, 1557–1567. Findings from the 2016 Conference on Machine Translation.” In Proceedings of the First Conference on Machine Translation, 131–198.
Precision writing as a form of speed training for students of translation.” Trainer of Translator and Interpreters. A Linguistic Evaluation of Rule-Based, Phrase-Based, and Neural MT Engines." Prague Bulletin of Mathematical Linguistics. Is Neural Machine Translation the New State of the Art?" Prague Bulletin of Mathematical Linguistics.
Statistical Machine Translation in the Translation Curriculum: Overcoming Barriers and Empowering Translators.” Trainer of Interpreters and Translators. Translators and machine translation: Gaps in knowledge and skills in translation pedagogy.” Trainer of interpreters and translators. A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localization Context.” Prague Bulletin of Mathematical Linguistics 93: 7–16.
Compare language-related issues for NGV and PBMT between German and English.” The Prague Bulletin of Mathematical Linguistics. Neural Machine Translation by Collectively Learning to Align and Translate.” In Proceedings of the 54th annual meeting of the Association for Computational Linguistics Berlin: Association for Computational Linguistics.
Target Text
Kenny ve Doherty'nin (2014) işaret ettiği gibi, çevirmenin NRF hazırlık, eğitim ve post-düzenleme süreçlerindeki becerilerine fayda sağlayabilecek potansiyel müdahale noktaları NRF için hala geçerlidir. 2017a), yeterlilik testi ve sonradan düzenleme sonrasında tutarsız değerlendirme sonuçları buldu ve üç alanda birden fazla dil çiftinde NRF çıktısında nispeten daha yüksek sayıda tanımlanmış eksik çeviri, sözcük ekleme ve yanlış çeviri hatası buldu. Bir bütün olarak çeviri endüstrisi, BC ile sonradan düzenleme sonrasında ciro oranlarının arttığını bildirmeye devam ediyor (Lommel ve DePalma 2016).
Geçici iş yükü veya son düzenleme için harcanan zaman, çevirmen kullanıcılarının isteksizliğine rağmen sıklıkla makine çevirisinin bir çeviri yardımı olarak kullanışlılığını vurgulamak için kullanılır (Plitt ve Masselot 2010; Guerberof 2012). Bu öğrencilerin sınıf ortamında bir miktar çeviri deneyimi olmasına rağmen, düzenleme sonrası deneyimleri yoktu. Öğrencilerden on segment üzerinden her bir makine çevirisi paradigması için segment başına ortalama son düzenleme sürelerini hesaplamaları, her paradigma için ortalama segment yeterlilik puanını sağlamaları ve toplam hata sayısını segment sayısına bölerek hata türlerinin sıklığını belirtmeleri istendi ( belki 10).
Yazarlar, NRF kullanıldığında eksik ve yanlış çevirilerde veya düzenleme sonrası çalışmalarda belirgin bir gelişme bulamadılar. İlk gruptaki öğrencilerin %62'si NRF çıktısını işlemek için daha az zaman harcadı. Bir öğrenci çalışma kağıdına şunları yazdı: "Sinirsel Çeviri aracını daha etkili buldum, ancak elbette mükemmel değildi ve işlem sonrası bazı hataları düzeltmem 20 dakikamı aldı."
Öğrenci grubunun araştırma tasarımı konusunda hiçbir deneyimi yoktu ve görevleri tamamlama sırasının önemini ve bu sınırlı post-düzenleme deneyiminin etkilerini dikkate almamışlardı. Sorulduğunda çoğu kişi önce IMP değerlendirmesini tamamladıklarını, bunun da bu paradigma için daha yavaş işlem sonrası hızlara yol açmış olabileceğini söyledi. Öğrenciler ayrıca Krings (2001) tarafından geliştirilen üç iş yükü ölçümünden birini kullanarak düzenleme sonrası görevle tanıştırıldı ve düzenleme sonrası iş yükü kavramıyla tanıştırıldı.
Productivity and quality in post-editing output from translation memories and machine translation.” PhD diss.Universitat Rovira i Virgili. Traditional and Emerging Use Cases for Machine Translation.” In Translation Quality Assessment: From Principles to Practice, edited by J.
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