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Register a new userControlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models
تقييم التحكم بالمعرفة النحوية في نماذج اللغة الصينية الماندرين
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أظهر العمل السابق أن الإشراف الهيكلية يساعد نماذج اللغة الإنجليزية على تعلم التعميمات حول الظواهر النحوية مثل اتفاقية الفعل الفعل. ومع ذلك، فإنه لا يزال غير واضح إذا كان مثل هذا التحيز الاستقرائي ستحسن أيضا قدرة نماذج اللغة على تعلم التبعيات النحوية بلغات مختلفة من الناحية النموذجية. نحن هنا التحقيق في هذا السؤال في لغة الماندرين الصينية، والتي لديها نظام كتابة من مقدم من لفائف التروج، إلى حد كبير؛ ترتيب كلمة مختلفة و sparser التشكل من الإنجليزية. نحن ندرب LSTMS، ونواسيب الشبكة العصبية المتكررة، ونماذج لغة المحولات، ونماذج تحليل التلال المعلمة للمحول على مجموعات بيانات ماندرين الصينية بأحجام مختلفة. نقيم قدرة النماذج على تعلم جوانب مختلفة من قواعد اللغة الماندرين التي تقييم العلاقات النحوية والدالة. نجد أدلة منهية أن الإشراف الهيكلية يساعد في تمثيل الحالة النحوية عبر المحتوى المتداخلة ويحسن الأداء في إعدادات البيانات المنخفضة، مما يشير إلى أن فوائد التحيزات الاستقرائي التسلسل الهرمي في الحصول على علاقات التبعية قد تتجاوز الإنجليزية.
Prior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement. However, it remains unclear if such an inductive bias would also improve language models' ability to learn grammatical dependencies in typologically different languages. Here we investigate this question in Mandarin Chinese, which has a logographic, largely syllable-based writing system; different word order; and sparser morphology than English. We train LSTMs, Recurrent Neural Network Grammars, Transformer language models, and Transformer-parameterized generative parsing models on two Mandarin Chinese datasets of different sizes. We evaluate the models' ability to learn different aspects of Mandarin grammar that assess syntactic and semantic relationships. We find suggestive evidence that structural supervision helps with representing syntactic state across intervening content and improves performance in low-data settings, suggesting that the benefits of hierarchical inductive biases in acquiring dependency relationships may extend beyond English.
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The factual knowledge acquired during pre-training and stored in the parameters of Language Models (LMs) can be useful in downstream tasks (e.g., question answering or textual inference). However, some facts can be incorrectly induced or become obsol
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