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Register a new userAn Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages
طريقة غير منشأة لبناء جملة تبسيط كورسا بلغات متعددة
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توفر تبسيط الجملة المتوازي (SS) نادرة لأوصوامل SS العصبية. نقترح طريقة غير منشأة لبناء SS Corpora من Translation Translation ثنائي اللغة واسعة النطاق، مما يخفف من الحاجة إلى SS Corporged Corge. يتم تحفيز طريقتنا عن طريق النتائج التالية: يميل نموذج الترجمة الآلية العصبية عادة إلى توليد المزيد من الرموز عالية التردد وفرق مستويات التعقيد النصية موجودة بين المصدر واللغة المستهدفة ل Translation Corpus. من خلال أخذ زوج من المصدر الجمل من Corpus Translation وترجمات مراجعها في لغة الجسر، يمكننا إنشاء بيانات SS موازية زائفة واسعة النطاق. بعد ذلك، نبقي أزواج الجملة هذه مع اختلاف أعلى تعقيد كزواج من جملة SS. يمكن أن تلبي المبنى SS Corpora مع نهج غير مدفوع التوقعات بأن الأحكام المحاذاة تحافظ على نفس المعاني وأن يكون لها اختلاف في مستويات تعقيد النص. تظهر النتائج التجريبية أن أساليب SS التي تدربت بها كوربورا تحقق النتائج من أحدث النتائج وتفوق النتائج على نتائج اللغة الإنجليزية في Wikilarge.
The availability of parallel sentence simplification (SS) is scarce for neural SS modelings. We propose an unsupervised method to build SS corpora from large-scale bilingual translation corpora, alleviating the need for SS supervised corpora. Our method is motivated by the following two findings: neural machine translation model usually tends to generate more high-frequency tokens and the difference of text complexity levels exists between the source and target language of a translation corpus. By taking the pair of the source sentences of translation corpus and the translations of their references in a bridge language, we can construct large-scale pseudo parallel SS data. Then, we keep these sentence pairs with a higher complexity difference as SS sentence pairs. The building SS corpora with an unsupervised approach can satisfy the expectations that the aligned sentences preserve the same meanings and have difference in text complexity levels. Experimental results show that SS methods trained by our corpora achieve the state-of-the-art results and significantly outperform the results on English benchmark WikiLarge.
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