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على الترجمة الآلية لاستعراضات المستخدم
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يحقق هذا العمل في أنظمة الترجمة الآلية العصبية (NMT) لترجمة مراجعات المستخدم الإنجليزية إلى الكرواتية والصربية، وهنايتان لغات معقدة مماثلة مورفولوجية. يتم استخدام نوعين من المراجعات لاختبار الأنظمة: تقييم الأفلام IMDB ومراجعات منتجات الأمازون. يتم استكشاف نوعين من بيانات التدريب: كورسا متوازية كبيرة من النطاق، بالإضافة إلى كوربوس الموازية الاصطناعية الصغيرة التي تم الحصول عليها بواسطة الترجمة الآلية لمراجعات الأمازون الإنجليزية أحادية الأمازون في اللغات المستهدفة. تظهر كل من الدرجات التلقائية والتقييم البشري أن استخدام Corpus في المجال الصناعي مع مجموعة فرعية مختارة من بيانات خارج المجال هو الخيار الأفضل. تشير النتائج المنفصلة على مراجعات IMDB و Amazon إلى أن أنظمة MT تؤدي بشكل مختلف عن أنواع المراجعة المختلفة بحيث لا ينبغي اعتبار مراجعات المستخدمين بشكل عام كندي متجانس. ومع ذلك، فإن البحث أكثر تفصيلا حول أكبر قدر من المراجعات المختلفة التي تغطي النطاقات / الموضوعات المختلفة ضرورية لفهم هذه الاختلافات بشكل كامل.
This work investigates neural machine translation (NMT) systems for translating English user reviews into Croatian and Serbian, two similar morphologically complex languages. Two types of reviews are used for testing the systems: IMDb movie reviews and Amazon product reviews. Two types of training data are explored: large out-of-domain bilingual parallel corpora, as well as small synthetic in-domain parallel corpus obtained by machine translation of monolingual English Amazon reviews into the target languages. Both automatic scores and human evaluation show that using the synthetic in-domain corpus together with a selected sub-set of out-of-domain data is the best option. Separated results on IMDb and Amazon reviews indicate that MT systems perform differently on different review types so that user reviews generally should not be considered as a homogeneous genre. Nevertheless, more detailed research on larger amount of different reviews covering different domains/topics is needed to fully understand these differences.
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