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Register a new userRevisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot
إعادة النظر في إعادة صياغة البداية المستندة إلى المحورية: اللغة ليست المحور الاختياري الوحيد
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الرجوع إلى النصوص التي تنقل نفس المعنى بأشكال تعبير مختلفة. أظهرت الأساليب المستندة إلى Pivot، المعروف أيضا باسم ترجمة الرحلة المستديرة، نتائج واعدة في توليد صياغة عالية الجودة. ومع ذلك، فإن الأساليب القائمة على المحور القائمة تعتمد جميعها على اللغة مثل المحور، حيث تكون النصوص الثنائية الثنائية الموازية على نطاق واسع، مطلوبة موازية عالية الجودة. في هذه الورقة، نستكشف جدوى استخدام التمثيلات الدلالية والنزاهة كحوري من أجل إعادة صياغة نصبها. بشكل ملموس، نتحول جملة إلى مجموعة متنوعة من التمثيلات الدلالية أو النحوية المختلفة (بما في ذلك AMR، UD، وتمثيل الدلالي الكامن)، ثم فك تشفير الجملة من التمثيل الدلالي. نسمح أيضا نهجا يستند إلى إعادة الاحتجاط بضغط عملية خط الأنابيب في إطار نهاية إلى نهاية. نقوم بإجراء تجارب تقارن مناهج مختلفة مع أنواع مختلفة من المحاور. تظهر النتائج التجريبية أن اتخاذ AMR كما يمكن للمحافظة الحصول على صياغة مع جودة أفضل من تناول اللغة كمحور. يمكن للإطار الطرفي النهائي أن يقلل من التحول الدلالي عند استخدام اللغة كحوري. إضافة إلى جانب ذلك، يمكن أن تولد العديد من الطرق القائمة على المحور غير المركبات أن تولد صياغة مع جودة مماثلة كنموذج التسلسل الإشراف على التسلسل، والذي يشير إلى أن البيانات الموازية للصيانة قد لا تكون ضرورية لإعادة صياغة عناصر التوليد.
Paraphrases refer to texts that convey the same meaning with different expression forms. Pivot-based methods, also known as the round-trip translation, have shown promising results in generating high-quality paraphrases. However, existing pivot-based methods all rely on language as the pivot, where large-scale, high-quality parallel bilingual texts are required. In this paper, we explore the feasibility of using semantic and syntactic representations as the pivot for paraphrase generation. Concretely, we transform a sentence into a variety of different semantic or syntactic representations (including AMR, UD, and latent semantic representation), and then decode the sentence back from the semantic representations. We further explore a pretraining-based approach to compress the pipeline process into an end-to-end framework. We conduct experiments comparing different approaches with different kinds of pivots. Experimental results show that taking AMR as pivot can obtain paraphrases with better quality than taking language as the pivot. The end-to-end framework can reduce semantic shift when language is used as the pivot. Besides, several unsupervised pivot-based methods can generate paraphrases with similar quality as the supervised sequence-to-sequence model, which indicates that parallel data of paraphrases may not be necessary for paraphrase generation.
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