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Oteandnn: تقدير شفافية تقادمها مع شبكة عصبية اصطناعية
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لترجيل اللغة المنطوقة إلى المتوسطة المكتوبة، تمكن معظم الحروف الهجائية قاعدة صوتية لا لبس فيها.ومع ذلك، فقد نأت بعض أنظمة الكتابة أنفسهم من هذا المفهوم البسيط والعمل القليل من العمل في معالجة اللغة الطبيعية (NLP) على قياس المسافة.في هذه الدراسة، نستخدم نموذج شبكة عصبي اصطناعي (آن) لتقييم الشفافية بين الكلمات المكتوبة ونطقها، وبالتالي تسميته تقدير الشفافية الذاتية مع آن (Oteann).بناء على مجموعات البيانات المستمدة من قواميس ويكيميديا، ندربنا هذا النموذج واختبر هذا النموذج لتسجيل النسبة المئوية للتنبؤات الخاطئة في مهام الترجمة من PhoneMe-to-grapheme و grapheme-to-phoneme.كانت الدرجات التي تم الحصول عليها على 17 تقييدا تتماشى مع تقديرات الدراسات الأخرى.ومن المثير للاهتمام، أن النموذج قدم أيضا نظرة ثاقبة أخطاء نموذجية مصنوعة من المتعلمين الذين ينظرون فقط في الحكم الصوتي في القراءة والكتابة.
To transcribe spoken language to written medium, most alphabets enable an unambiguous sound-to-letter rule. However, some writing systems have distanced themselves from this simple concept and little work exists in Natural Language Processing (NLP) on measuring such distance. In this study, we use an Artificial Neural Network (ANN) model to evaluate the transparency between written words and their pronunciation, hence its name Orthographic Transparency Estimation with an ANN (OTEANN). Based on datasets derived from Wikimedia dictionaries, we trained and tested this model to score the percentage of false predictions in phoneme-to-grapheme and grapheme-to-phoneme translation tasks. The scores obtained on 17 orthographies were in line with the estimations of other studies. Interestingly, the model also provided insight into typical mistakes made by learners who only consider the phonemic rule in reading and writing.
References used
https://aclanthology.org/
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