{"id":3326,"date":"2020-10-07T08:58:34","date_gmt":"2020-10-07T08:58:34","guid":{"rendered":"https:\/\/asling.org\/tc42\/?page_id=3326"},"modified":"2020-11-17T17:59:57","modified_gmt":"2020-11-17T17:59:57","slug":"posters","status":"publish","type":"page","link":"https:\/\/asling.org\/tc42\/?page_id=3326","title":{"rendered":"Posters"},"content":{"rendered":"\t\t
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Posters<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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All posters on 20 Nov. starting at 11:15 (GMT)<\/span><\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Asma Alamri<\/h3><\/p>\n

University of Birmingham<\/strong><\/p><\/center>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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<\/a>Google Translate and Children’s Literature. <\/span><\/strong><\/span>
Can Google Translate do it alone, without Post-Editing?<\/span><\/strong><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Since the 1980s, machine translation (MT) technology has shown significant improvements and has been introduced as another type of translation aid, rather than aiming to produce a fully automatic high-quality translation. Since raw machine translation output cannot always meet the needs of the end users, it has been combined with post-editing. The question here is whether machine translation combined with post-editing is good enough to rely on for the translation of children\u2019s stories? Arnold points out that \u201call we want from the computer is some kind of draft quality translation: something which is more or less faithful to the original, understandable in its own right, and which is a reasonable starting point for a polished translation\u201d (2003:113). However, MT output cannot be reliable on its own as it varies significantly \u201cdepending on the system and language pair and still far from human translation (HT) standards\u201d (Carl et al, 2015). So, machine translation post-editing can be considered as a preferential step to improve MT output. It has been argued that using MT tools helps to foster the process of translation without reducing its quality if it is combined with post-editing, and this is what this paper aims to prove.<\/p> \n

This paper investigates the claim that raw machine translation output combined with post-editing can be as good as human standard translation, taking into consideration the benefits of using machine translation: saving time and ensuring translation quality. This investigation will be done on a children\u2019s story text of 509 words for the language pair English\u2192 Arabic. The story will be translated using Google Translate and edited manually. To evaluate the quality of the post-edited text, I will translate the story from scratch and compare both texts.<\/p> \n

At first sight, children\u2019s stories appear to be simple and direct with simplified vocabulary and grammar, but in fact to translate them with faithfulness can be quite a challenge (Van Doorslaer, 2012:23). I chose to translate this text because children\u2019s literature has special requirements in translation. Beside the semantic and lexical challenges that face machine translation these texts have more to examine. They demand cultural and ideological adjustment. Also, the difference in the tone and the stylistic and aesthetic aspects of the stories\u2019 presentation make translating them challenging to enable them to be read like an original,non-translated text, which is another challenge.<\/p>\n

For translating my text, I chose Google Translate which uses a neural machine translation NMT engine and is available for free. Google Translate translates whole sentences and looks at the broader context to guess the most relevant translation. It does this by going through the pre-translated database it has. It is widely used as it was shown in the latest statistics released in 2018: Google Translate boasts more than 500m daily users worldwide and offers 103 languages (Sommerlad, 2018).<\/p> \n

In Arabic, children\u2019s stories vocabularies should be simplified, so they match young children\u2019s language levels. Machine translation engines usually provide translation in classic Arabic, which needs to be replaced with a simplified language that is adapted to the target audience. In addition, in children\u2019s stories rhyme is essential, as children\u2019s stories target young children to instil values; they are also read aloud, so they have rhyme which makes them lovely to hear. Also, children\u2019s literature translation is not easy because it is not intended to be presented to children exclusively. It is also targeted at parents, teachers and librarians. This \u201cdual readership\u201d makes it more demanding when it comes to translation and reproduction (Alvstad 2010). Given the above reasons, it is clear that children\u2019s stories require high quality translation.<\/p> \n

By the end of this investigation, a new role of translators can be seen. Machine translation has transformed the process of translation and upgraded the role of translators to be post-editors, which was realised by the National Degree Committee in China that offered Master programs and training courses for translators \u201cas demanded by the market\u201d to fill this gap in the increasing need for the language services industry (Jia et al, 2019). In the meantime, the role of machine translation cannot be denied or ignored. NMT\u2019s characteristic improvements in grammar and coherence help translators to spend less time editing the text and polishing it rather than spending longer time in translating it from scratch.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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Asma Alamri is a freelance translator with a Master\u2019s degree in Translation Studies obtained from University of Birmingham (UK). She combins the degree of higher Diploma in General Education with the Master\u2019s degree in translation studies to translate children\u2019s literature in the language pair English-Arabic. This article was written under the supervision of Dr. Gabriella Saldanha.<\/p> \n\n