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Register a new userControllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer
تبسيط الجملة يمكن السيطرة عليها مع محول نقل نص إلى نص موحد
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في الآونة الأخيرة، حقق نموذج لغوي كبير مدرب مسبقا يسمى T5 (محول نقل النصوص الموحد للنصوص) أداء حديثة في العديد من مهام NLP.ومع ذلك، لم يتم العثور على أي دراسة باستخدام هذا النموذج المدرب مسبقا على تبسيط النص.لذلك في هذه الورقة، نستكشف استخدام T5 Brick-Tuning على تبسيط النص الجمع بين آلية يمكن التحكم فيها لتنظيم مخرجات النظام التي يمكن أن تساعد في إنشاء نص مكيفات للجماهير المستهدفة المختلفة.تبين تجاربنا أن نموذجنا يحقق نتائج رائعة مع مكاسب بين +0.69 و +1.41 عبر أحدث الولاية الحالية (بارت + الوصول).نقول أن استخدام نموذج مدرب مسبقا مثل T5، المدربين على عدة مهام مع كميات كبيرة من البيانات، يمكن أن يساعد في تحسين تبسيط النص.
Recently, a large pre-trained language model called T5 (A Unified Text-to-Text Transfer Transformer) has achieved state-of-the-art performance in many NLP tasks. However, no study has been found using this pre-trained model on Text Simplification. Therefore in this paper, we explore the use of T5 fine-tuning on Text Simplification combining with a controllable mechanism to regulate the system outputs that can help generate adapted text for different target audiences. Our experiments show that our model achieves remarkable results with gains of between +0.69 and +1.41 over the current state-of-the-art (BART+ACCESS). We argue that using a pre-trained model such as T5, trained on several tasks with large amounts of data, can help improve Text Simplification.
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Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained
end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing systems, and can control the degree of each simplification operation applied to the input texts.
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The quality of fully automated text simplification systems is not good enough for use in real-world settings; instead, human simplifications are used. In this paper, we examine how to improve the cost and quality of human simplifications by leveragin
g crowdsourcing. We introduce a graph-based sentence fusion approach to augment human simplifications and a reranking approach to both select high quality simplifications and to allow for targeting simplifications with varying levels of simplicity. Using the Newsela dataset (Xu et al., 2015) we show consistent improvements over experts at varying simplification levels and find that the additional sentence fusion simplifications allow for simpler output than the human simplifications alone.
We present CoTexT, a pre-trained, transformer-based encoder-decoder model that learns the representative context between natural language (NL) and programming language (PL). Using self-supervision, CoTexT is pre-trained on large programming language
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