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Register a new userModeling Event Plausibility with Consistent Conceptual Abstraction
نمذجة معقولية الحدث مع التجريد المفاهيمي المتسق
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يتطلب فهم اللغة الطبيعية الحس السليم، وهو جانب واحد منها هو القدرة على تمييز معقول الأحداث.في حين أن نماذج التوزيع --- أحدث نماذج لغة محول مؤخرا --- - - أظهرت تحسينات في حالة قدر نفواد الحدث، فإن أدائها لا يزال أقل من البشر.في هذا العمل، نظهر أن نماذج المعقول القائم على المحولات لا تتفق عليها بشكل ملحوظ عبر الفصول المفاهيمية للتسلسل الهرمي المعجمي، مما يستنتج أن الشخص يتنفس "من المعقول بينما يتنفس طبيب الأسنان" ليس كذلك، على سبيل المثال.نجد أن هذا التناقض يستمر حتى عندما يتم حقن الطراجات بهدوء مع المعرفة المعجمية، ونقدم طريقة بسيطة ما بعد الهوك لإجبار الاتساق النموذجي الذي يحسن الارتباط مع أحكام الصفقات البشرية.
Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models---most recently pre-trained, Transformer language models---have demonstrated improvements in modeling event plausibility, their performance still falls short of humans'. In this work, we show that Transformer-based plausibility models are markedly inconsistent across the conceptual classes of a lexical hierarchy, inferring that a person breathing'' is plausible while a dentist breathing'' is not, for example. We find this inconsistency persists even when models are softly injected with lexical knowledge, and we present a simple post-hoc method of forcing model consistency that improves correlation with human plausibility judgements.
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