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Register a new userBiSECT: Learning to Split and Rephrase Sentences with Bitexts
Bisect: تعلم تقسيم وجمل إعادة صياغة مع Bitex
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مهمة مهمة في تطبيقات NLP مثل تبسيط الجملة هي القدرة على اتخاذ جملة طويلة ومعقدة وتقسيمها إلى جمل أقصر، وإعادة صياغة حسب الضرورة. نقدم مجموعة بيانات جديدة ونموذج جديد لهذه المهمة الانقسام وإعادة صياغة. تتكون بياناتنا في Bisect التدريبية من 1 مليون جمل إنجليزية طويلة مقترن بأجمل الإنجليزية الأقصر والمعاواة بينها. نحصل على هؤلاء من خلال استخراج محاذاة جملة واحدة في فورانيا متوازية ثنائية اللغة ثم استخدام الترجمة الآلية لتحويل كلا الجانبين من الجور إلى نفس اللغة. يحتوي Bisect على أمثلة تدريبية ذات جودة أعلى من SPORTA SPORTA السابق وإعادة صياغتها، مع انشقاقات الجملة التي تتطلب تعديلات أكثر أهمية. نقوم بتصنيف أمثلة في Corpus لدينا واستخدام هذه الفئات في نموذج جديد يتيح لنا استهداف مناطق محددة من جملة المدخلات التي سيتم تقسيمها وتحريرها. علاوة على ذلك، نوضح أن النماذج المدربة على Bisect يمكن أن تؤدي مجموعة متنوعة واسعة من العمليات المنقسمة وتحسينها على النهج السابقة للحالة السابقة في التقييمات التلقائية والبشرية.
An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this split and rephrase' task. Our BiSECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BiSECT contains higher quality training examples than the previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BiSECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations.
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