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Register a new userEDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints
المحرر: محول تحرير يعتمد على إعادة وضع الترجمة الآلية العصبية مع القيود المعجمية الناعمة
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مجردة، نقدم محول تحرير يعتمد على إعادة تحديد موضع (محرر)، مما يجعل توليد التسلسل مرنا بسلاسة يسمح للمستخدمين بسلاسة لتحديد التفضيلات في الاختيار المعجمي الإخراج.بناء على النماذج الأخيرة لتوليد التسلسل غير التلقائي (GU al.، 2019)، يولد المحرر تسلسلات جديدة من خلال تحرير الفرضيات الإثارة.يعتمد على عملية "إعادة وضع رواية" مصممة لتفكيك الاختيار المعجمي من قرارات تحديد المواقع Word، مع تمكين الأوراج الفعالة للتعلم التقليد والتحرير الموازي في وقت فك التشفير.من التجريبية، يستخدم المحرر القيود المعجمية الناعمة بشكل أكثر فعالية من محول Levenshtein (Gu et al.، 2019) أثناء تسريع فك التشفير بشكل كبير مقارنة بشكل كبير بالبحث عن شعاع (Post and Vilar، 2018).يحقق المحرر أيضا جودة ترجمة قابلة للمقارنة أو أفضل مع سرعة فك التشفير أسرع من مهام الترجمة الآلية الرومانية والإنجليزية والإنجليزية والإنجليزية.
Abstract We introduce an Edit-Based TransfOrmer with Repositioning (EDITOR), which makes sequence generation flexible by seamlessly allowing users to specify preferences in output lexical choice. Building on recent models for non-autoregressive sequence generation (Gu et al., 2019), EDITOR generates new sequences by iteratively editing hypotheses. It relies on a novel reposition operation designed to disentangle lexical choice from word positioning decisions, while enabling efficient oracles for imitation learning and parallel edits at decoding time. Empirically, EDITOR uses soft lexical constraints more effectively than the Levenshtein Transformer (Gu et al., 2019) while speeding up decoding dramatically compared to constrained beam search (Post and Vilar, 2018). EDITOR also achieves comparable or better translation quality with faster decoding speed than the Levenshtein Transformer on standard Romanian-English, English-German, and English-Japanese machine translation tasks.
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The paper presents experiments in neural machine translation with lexical constraints into a morphologically rich language. In particular and we introduce a method and based on constrained decoding and which handles the inflected forms of lexical ent
ries and does not require any modification to the training data or model architecture. To evaluate its effectiveness and we carry out experiments in two different scenarios: general and domain-specific. We compare our method with baseline translation and i.e. translation without lexical constraints and in terms of translation speed and translation quality. To evaluate how well the method handles the constraints and we propose new evaluation metrics which take into account the presence and placement and duplication and inflectional correctness of lexical terms in the output sentence.
Recently a number of approaches have been proposed to improve translation performance for document-level neural machine translation (NMT). However, few are focusing on the subject of lexical translation consistency. In this paper we apply one transla
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