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Register a new userSimple induction of (deterministic) probabilistic finite-state automata for phonotactics by stochastic gradient descent
تحريض بسيط من (الحتمية) أتمتة الحالة المحدودة الواحد للاتحاد الفوني من خلال نزول التدرج الاستوكاستك
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نحن نقدم متعلما فونوتياكيا بسيطا وعالي للغاية يقوم بتحفيز شركة Automaton المحدودة المحدودة من بيانات نموذج Word.نحن تصف المتعلم وإظهار كيفية قيامه بتحديده للحث على اللغات العادية غير المقيدة، وكذلك كيفية تقييدها إلى بعض الفصول الدراسية غير النظامية مثل لغات K-Local K-Local و K- الدقيقة.نقيم المتعلم في قدرته على تعلم القيود الشوئية في أمثلة اللعبة وفي مجموعات بيانات Quechua و Navajo.نجد أن المتعلم غير المقيد هو الأكثر دقة بشكل عام عند النمذجة أشكال تشهد في التدريب؛ومع ذلك، فإن المتعلم الذي يقتصر فقط على فئة اللغة بالعرق الصارم يلتقط بنجاح بعض القيود الفونية غير المخاطية.يخدم المتعلم لدينا كأساس أساليب أكثر تطورا.
We introduce a simple and highly general phonotactic learner which induces a probabilistic finite-state automaton from word-form data. We describe the learner and show how to parameterize it to induce unrestricted regular languages, as well as how to restrict it to certain subregular classes such as Strictly k-Local and Strictly k-Piecewise languages. We evaluate the learner on its ability to learn phonotactic constraints in toy examples and in datasets of Quechua and Navajo. We find that an unrestricted learner is the most accurate overall when modeling attested forms not seen in training; however, only the learner restricted to the Strictly Piecewise language class successfully captures certain nonlocal phonotactic constraints. Our learner serves as a baseline for more sophisticated methods.
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Shupamem, a language of Western Cameroon, is a tonal language which also exhibits the morpho-phonological process of full reduplication. This creates two challenges for finite-state model of its morpho-syntax and morphophonology: how to manage the fu
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