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Register a new userEvaluating the Morphosyntactic Well-formedness of Generated Texts
تقييم مورفوسنكتاكي شكل جيد للنصوص المتولدة
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نظم توليد النص في كل مكان في تطبيقات معالجة اللغة الطبيعية.ومع ذلك، فإن تقييم هذه النظم يظل تحديا، خاصة في إعدادات متعددة اللغات.في هذه الورقة، نقترح L'Ambre - مقياس قياسي لتقييم صورة نصية مورفوسنكتاسية من النص باستخدام تحليل التبعية والقواعد المورفوسنكتانية للغة.نقدم طريقة لاستخراج القواعد المختلفة التي تحكم morphosyntax مباشرة من Temessency Treebanks.لمعالجة النواتج الصاخبة من أنظمة جيل النص، نقترح منهجية بسيطة لتدريب المحللين القويين.نظهر فعالية قيادةنا في مهمة الترجمة الآلية من خلال دراسة DIACHRONIC للنظم ترجمة إلى لغات غنية بالمظورة.
Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L'AMBRE -- a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.
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