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Register a new userInstance-Based Neural Dependency Parsing
تحليل التبعية العصبية القائمة على المثيل
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المعردات التفسيرية مجردة للتوقعات النموذجية هي حاسمة في التطبيقات العملية.نحن نطور النماذج العصبية التي تمتلك عملية استنتاجية مفسضة لتحليل التبعية.تتبنى نماذجنا الاستدلال المستند إلى المثيل، حيث يتم استخراج حواف التبعية ومسمى من خلال مقارنةها بالحواف في مجموعة تدريبية.يتم استخدام حواف التدريب صراحة للتنبؤات؛وبالتالي، من السهل فهم مساهمة كل حافة إلى التنبؤات.تظهر تجاربنا أن النماذج القائمة على مثيل لدينا تحقق دقة تنافسية مع النماذج العصبية القياسية ولديها المعقول المعقولة من التفسيرات القائمة على المثيل.
Abstract Interpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency edges are extracted and labeled by comparing them to edges in a training set. The training edges are explicitly used for the predictions; thus, it is easy to grasp the contribution of each edge to the predictions. Our experiments show that our instance-based models achieve competitive accuracy with standard neural models and have the reasonable plausibility of instance-based explanations.
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