في حين أن Yu و Poesio (2020) قد أظهر مؤخرا تفوق نموذج تعلم المهام العصبي المتعدد المهام (MTL) إلى النهج القائمة على القواعد لسد قرار أنشفورا، فهناك القليل من الفهم (1) كيف يكون أفضل من القواعدالأساليب (على سبيل المثال، هي النهجتين التي تصنع أخطاء مماثلة أو تكميلية؟) و (2) ما ينبغي تحسينه.لإلقاء الضوء على هذه القضايا، نقترحنا (1) نهجا هجينا مقرا له على القواعد ومتناهية الديمقراطية من شأنه أن تمكن فهم أفضل لنقاط القوة والضعف المقارنة؛و (2) قم بإجراء تحليل يدوي للأخطاء التي يقوم بها نموذج MTL.
While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.
References used
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