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مسودة الدلالية المجانية تقريبا للترجمة الآلية العصبية
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يمكن تحسين جودة الترجمة من خلال المعلومات العالمية من الجملة المستهدفة المطلوبة لأن وحدة فك الترميز يمكن أن تفهم كل من المعلومات السابقة والمستقبلية.ومع ذلك، يحتاج النموذج إلى تكلفة إضافية لإنتاج والنظر في هذه المعلومات العالمية.في هذا العمل، لحقن معلومات عالمية ولكن أيضا توفير التكلفة، نقدم طريقة فعالة للعينة والنظر في مشروع دلالي كمعلومات عالمية من الفضاء الدلالي ل فكيبها مع خالية من التكلفة تقريبا.على عكس التكيفات الناجحة الأخرى، لا يتعين علينا تنفيذ عملية تشبه ممن عينات مرارا وتكرارا من الفضاء الدلالي المحتمل.تظهر التجارب التجريبية أن الطريقة المقدمة يمكن أن تحقق أداء تنافسي في أزواج اللغة المشتركة مع ميزة واضحة في كفاءة الاستدلال.سنفتح جميع التعليمات البرمجية المصدر الخاصة بنا على Github.
Translation quality can be improved by global information from the required target sentence because the decoder can understand both past and future information. However, the model needs additional cost to produce and consider such global information. In this work, to inject global information but also save cost, we present an efficient method to sample and consider a semantic draft as global information from semantic space for decoding with almost free of cost. Unlike other successful adaptations, we do not have to perform an EM-like process that repeatedly samples a possible semantic from the semantic space. Empirical experiments show that the presented method can achieve competitive performance in common language pairs with a clear advantage in inference efficiency. We will open all our source code on GitHub.
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Domain Adaptation is widely used in practical applications of neural machine translation, which aims to achieve good performance on both general domain and in-domain data. However, the existing methods for domain adaptation usually suffer from catast
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