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Register a new userLearning to Recognize Dialect Features
تعلم التعرف على ميزات اللهجة
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بناء أنظمة NLP التي تخدم الجميع يتطلب محاسبة اختلافات اللهجة. ولكن اللهجات ليست كيانات متجانسة: بالأحرى، يتم التقاط الفروق بين اللهجات وداخلها من خلال وجود وغياب العشرات من ميزات اللهجة في الكلام والنص، مثل حذف كوبولا في إنه ∅ قيد التشغيل ". في هذه الورقة، نقدم مهمة الكشف عن ميزة اللهجة، وتقديم نهج تعليمي متعدد الأيتاكي، بناء على المحولات المحددة مسبقا. بالنسبة لمعظم اللهجات، لا تتوفر كوربورا المشروح على نطاق واسع لهذه الميزات، مما يجعل من الصعب تدريب التعرف عليها. نحن ندرب نماذجنا على عدد صغير من أزواج صغيرة، والبناء على كيفية تحديد اللغويين عادة ميزات اللهجة. التقييم في مجموعة اختبار من 22 ميزات لهجيات للهنديون يدل على أن هذه النماذج تتعلم كيفية التعرف على العديد من الميزات بدقة عالية، وأن عدد قليل من أزواج الحد الأدنى يمكن أن تكون فعالة للتدريب مع الآلاف من الأمثلة المسمى. ونحن نوضح أيضا إمكانية تطبيق ميزة الهياكل على حد سواء كإجراء من كثافة اللهجة وكمعشر لهجة اللهجة.
Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in He ∅ running''. In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers. For most dialects, large-scale annotated corpora for these features are unavailable, making it difficult to train recognizers. We train our models on a small number of minimal pairs, building on how linguists typically define dialect features. Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples. We also demonstrate the downstream applicability of dialect feature detection both as a measure of dialect density and as a dialect classifier.
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