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Register a new userPCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols
يمكن أن تفعل PCFGS بشكل أفضل: حث النحو من السياق الحاصل مع العديد من الرموز
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تبين أن قواعد النحوية الخالية من السياق (PCFGS) مع المعلمة العصبية فعالة في تحريض قواعد العبارات غير المعروضة.ومع ذلك، نظرا للتعقيد المركزي المكعبي لتمثيل PCFG وتحليله، فإن النهج السابقة لا يمكن أن توسيع نطاق عدد كبير نسبيا من الرموز (غير اللامعة والأعمدة).في هذا العمل، نقدم شكل معلمات جديد من PCFGS استنادا إلى تحلل تربوت، والذي يحتوي على تعقيد حسابي ثلاثي في رقم الرمز، وبالتالي يسمح لنا باستخدام عدد أكبر بكثير من الرموز.نحن نستخدم أيضا المعلمة العصبية للنموذج الجديد لتحسين أداء تحليل غير مخالف.نقيم نموذجنا عبر عشرة لغات وإظهار تجريبيا فعالية استخدام المزيد من الرموز.
Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown to be effective in unsupervised phrase-structure grammar induction. However, due to the cubic computational complexity of PCFG representation and parsing, previous approaches cannot scale up to a relatively large number of (nonterminal and preterminal) symbols. In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols. We further use neural parameterization for the new form to improve unsupervised parsing performance. We evaluate our model across ten languages and empirically demonstrate the effectiveness of using more symbols.
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