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مشاركة Nectec في WAT-2021
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في هذه الورقة، نبلغ عن النتائج التجريبية لنماذج الترجمة الآلية التي أجرتها فريق Nectec لمهام ترجمة WAT-2021. أساسا، تستند نماذجنا إلى الأساليب العصبية لكلا اتجاهين أزواج اللغة الإنجليزية ميانمار وميانمار الإنجليزية. تركز معظم نماذج الترجمة العصبية الحالية (NMT) بشكل أساسي على تحويل البيانات المتسلسلة ولا تستخدم المعلومات الأساسية مباشرة. ومع ذلك، فإننا نقوم بإجراء نماذج الترجمة الآلية المتعددة المصدر (NMT) باستخدام Corpora متعددة اللغات مثل Corpus State Data Corpus أو Corpus Data Data Corpus، أو Corpus Data Data Pos-Deagged. الترجمة متعددة المصادر هي نهج لاستغلال مدخلات متعددة (على سبيل المثال بتنسيقتين مختلفتين) لزيادة دقة الترجمة. تم تنفيذ نموذج ترميز التشفير القائم على RNN مع آلية الاهتمام وبنية المحولات لتجرينا. أظهرت النتائج التجريبية أن النماذج المقترحة من الهندسة المعمارية القائمة على RNN تتفوق على نموذج خط الأساس لمهمة الترجمة الإنجليزية إلى ميانمار، ونماذج المحولات متعددة المصدر والمشتركة متعددة المصدر تحقق نتائج ترجمة أفضل من الأساس.
In this paper, we report the experimental results of Machine Translation models conducted by a NECTEC team for the translation tasks of WAT-2021. Basically, our models are based on neural methods for both directions of English-Myanmar and Myanmar-English language pairs. Most of the existing Neural Machine Translation (NMT) models mainly focus on the conversion of sequential data and do not directly use syntactic information. However, we conduct multi-source neural machine translation (NMT) models using the multilingual corpora such as string data corpus, tree data corpus, or POS-tagged data corpus. The multi-source translation is an approach to exploit multiple inputs (e.g. in two different formats) to increase translation accuracy. The RNN-based encoder-decoder model with attention mechanism and transformer architectures have been carried out for our experiment. The experimental results showed that the proposed models of RNN-based architecture outperform the baseline model for English-to-Myanmar translation task, and the multi-source and shared-multi-source transformer models yield better translation results than the baseline.
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