The Machine Translation of Literature: Implications for Translation Pedagogy

Abstract

The recent years have witnessed an increasing importance of machine translation systems due to the prolific production on online texts in different disciplines and furthermore, the inability of traditional translation methods in addressing translation needs all over the world. It is even argued that training on translation tools should be integrated into translation pedagogies and ultimately, courses should be provided for students and professionals. In spite of the effectiveness of translation tools and systems in providing solutions in relation to different disciplines and text genres, the usability and reliability of such systems in terms of literary texts, however, is still highly controversial. Many critics and educators still underestimate the usefulness of the machine translation systems in literature, which could be partially attributed to the unique nature of the language of the literary texts. The issue has its pedagogical implications to translation instruction due to the needs to integrate emerging technologies in teaching and learning practices. For proper use of translation technologies in educational contexts, these need to be well evaluated. For this purpose, this study evaluates the usefulness of applying machine translation systems to literature with the purpose of identifying the challenges that may have negative impacts on the reliability of machine translation systems. In order to do this this, two translation systems are selected, namely, Google Translate and Q Translate. By way of illustration, the study is based on a corpus of two English short stories. The study is based on two prose fiction texts. The first is J. K. Rowling’s novel Harry Potter and the Philosopher’s Stone. The second is Edgar Allan Poe’s short story The Black Cat. Automatic translations generated by the two machine translation systems were compared to human made Arabic translations with the purpose of identifying the problems within these translations. Results indicate that different lexical, structural, and pragmatic errors are encountered by users which negatively impact the reliability of these translations. Educators and translation instructors need to reflect on the challenges of machine translation systems in relation to literature. Software developers need also to address the problems faced by users and students in the translation from and into the Arabic language.

An Evaluation of the Accuracy of the Machine Translation Systems of Social Media Language

“…Likewise, Al-Dabbagh [31] disclosed that English-Arabic translation of varied text types via Google Translate produced the full range of errors from the lexical level up to the contextual level. Omar and Gomaa [32] questioned the reliability of GT due to the many errors surveyed that relate to different lexical, structural, and pragmatic types of errors. Hadla, et al [12] highlighted the tendency of MT systems including Google Translate to translate literally overlooking the pragmatic aspects.…”

“…The pure dialectness and the code switching between MSA and the Arabic dialect cause the system to work relatively improperly and render defective translations. In this regard, Omar and Gomaa [32] argue that the degree of colloquialism determines how accurate and reliable machine translation is due to the elastic nature of the dialectic coding system lexically or syntactically.…”

“…It is worthy to note that reviewing the studies that explored the MT performance concerning literary texts [32,44,45] and proverbs genre [18,45,46] have common denominator with social media texts. Though different in their type of texts, they all agree in that high degree of cultural aspects exists in them that determine the translation quality.…”

Takarir: A new simultaneous translator voice to text to promote bi/multilinguality

“…In this study, the differences in the translation results of the Takarir are more due to differences in the type and quality of the user's microphone. The different lexical could harm the result of the translation (Omar & Gomaa, 2020). As explained earlier, the translation results of Takarir depend on the quality of the input because Takarir is an MT based on the voice-to-text translation method.…”

The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

“…A survey by Wu and Zhang (2018) showed that compared with traditional English teaching, students who use machine translation have a better understanding of the classroom and help to improve translation skills [ 12 ]. Omar and Gomaa (2020) found that when students encounter difficult vocabulary, structure, and pragmatics in the classroom, the use of machine translation will have a negative impact on the reliability of these translations [ 13 ]. Läubli and Green (2019) [ 14 ] pointed out that the role of human-computer interaction should be fully utilized.…”