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Register a new userDiscourse Probing of Pretrained Language Models
خطاب التحقيق في نماذج اللغة المحددة مسبقا
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تركز العمل الحالي على التحقيق في نماذج اللغة المحددة مسبقا (LMS) في الغالب على المهام الأساسية على مستوى الجملة.في هذه الورقة، نقدم إجراء خطاب على مستوى المستندات لتقييم قدرة LMS المسبقة على التقاط العلاقات على مستوى المستندات.نقوم بتجربة 7 LMS محددة مسبقا، 4 لغات، و 7 مهام قيد الخطاب، والعثور على بارت ليكون بشكل عام أفضل نموذج في التقاط الخطاب - - ولكن فقط في تشفيرها، مع بيرت أداء بشكل مفاجئ نموذج الأساس.عبر النماذج المختلفة، هناك اختلافات كبيرة في أفضل طبقات في التقاط معلومات خطاب، والتفاوتات الكبيرة بين النماذج.
Existing work on probing of pretrained language models (LMs) has predominantly focused on sentence-level syntactic tasks. In this paper, we introduce document-level discourse probing to evaluate the ability of pretrained LMs to capture document-level relations. We experiment with 7 pretrained LMs, 4 languages, and 7 discourse probing tasks, and find BART to be overall the best model at capturing discourse --- but only in its encoder, with BERT performing surprisingly well as the baseline model. Across the different models, there are substantial differences in which layers best capture discourse information, and large disparities between models.
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