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Register a new userImproving the Diversity of Unsupervised Paraphrasing with Embedding Outputs
تحسين تنوع إعادة الصياغة غير المنشأة مع مخرجات التضمين
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نقدم تقنية جديدة لتوليد الصفر عن إعادة صياغة الصفر.المساهمة الرئيسية هي طراز إعادة صياغة متعددة اللغات من طرف تم تدريبه على استخدام كورسرا المتوازي المترجمة لتوليد الصياغة في المساحات المعنى "- استبدال طبقة SoftMax النهائية مع Adgeddings Word.يتيح هذا التعديل المعماري، بالإضافة إلى إجراء تدريبي يشتمل على هدف AutoNCoding، مع المعلمة الفعالة تقاسم لغات لمزيد من إعادة كتابة أحادي الأبعاد بطلاقة، ويسهل الطلاقة والتنوع في المخرجات التي تم إنشاؤها.تتفوق نماذج توليد الناتج المستمر الناتج عن إعادة صياغة خطوط خطوط خطوط إعادة صياغة صفرية عند تقييم لغتين باستخدام بطارية من المقاييس الحسابية وكذلك في التقييم البشري.
We present a novel technique for zero-shot paraphrase generation. The key contribution is an end-to-end multilingual paraphrasing model that is trained using translated parallel corpora to generate paraphrases into meaning spaces'' -- replacing the final softmax layer with word embeddings. This architectural modification, plus a training procedure that incorporates an autoencoding objective, enables effective parameter sharing across languages for more fluent monolingual rewriting, and facilitates fluency and diversity in the generated outputs. Our continuous-output paraphrase generation models outperform zero-shot paraphrasing baselines when evaluated on two languages using a battery of computational metrics as well as in human assessment.
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Previous works on syntactically controlled paraphrase generation heavily rely on large-scale parallel paraphrase data that is not easily available for many languages and domains. In this paper, we take this research direction to the extreme and inves
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