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Register a new userText Generation from Discourse Representation Structures
جيل النص من هياكل تمثيل الخطاب
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نقترح النماذج العصبية لتوليد نص من تمثيلات معناية رسمية بناء على هياكل تمثيل الخطاب (DRSS).DRSS هي تمثيلات على مستوى المستند والتي تشفص بالتفاصيل الدلالية الغنية المتعلقة بالعلاقات الخطابية، والافتراض، والتعايش التعاوني داخل وعبر الجمل.نقوم بإضفاء الطابع الرسمي على مهمة الجيل العصبي DRS إلى النص وتوفير حلول النمذجة لمشاكل طلب الشرط وتسمية التسمية المتغيرة التي تجعل الجيل من DRSS غير تافهة.يعتمد مولدنا على نموذج Treelstm الرواية القادرة على تمثيل هياكل DRS بدقة وهو مناسب بشكل عام للأشجار ذات فروع واسعة.نحقق أداء تنافسي (59.48 بلو) على معيار GMB ضد العديد من خطوط الأساس القوية.
We propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). DRSs are document-level representations which encode rich semantic detail pertaining to rhetorical relations, presupposition, and co-reference within and across sentences. We formalize the task of neural DRS-to-text generation and provide modeling solutions for the problems of condition ordering and variable naming which render generation from DRSs non-trivial. Our generator relies on a novel sibling treeLSTM model which is able to accurately represent DRS structures and is more generally suited to trees with wide branches. We achieve competitive performance (59.48 BLEU) on the GMB benchmark against several strong baselines.
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We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs). We use a rather standard bi-LSTM sequence-to-sequence model, work with a linearized DRS input repr
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