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Register a new userFrom compositional semantics to Bayesian pragmatics via logical inference
من دلالات التركيبية إلى باغماتيكيات البيئي عبر الاستدلال المنطقي
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توفر الدلالات الرسمية في تقليد مونتاجوفي صياغة معنى دقيقا، ولكن عادة دون نظرية رسمية من البراغماتية لمعايير السياق وحساستها لمعرفة الخلفية. وفي الوقت نفسه، تجعل النظريات الرسمية البراغماتية تنبؤات صريحة حول المعنى في السياق، ولكنها عموما دون دلالات تركيبية محددة جيدا. نقترح إطارا مشتركا للتفسير الدلالي والعملي للجمل في مواجهة المعرفة الاحتمالية. نحن نقوم بذلك (1) تمديد مخطط تفسير Montagovian لتوليد التوزيع عبر المعاني المحتملة، و (2) إنشاء خلفي لهذا التوزيع باستخدام متغير نماذج قانون الكلام الرشيد (RSA)، ولكن معمم على المقترحات التعسفية. يتم ربط هذه الجوانب من إطارنا معا من خلال تقييم الاستقصاء بموجب عدم اليقين الاحتمالي. نطبق نموذجنا على قرار أنشفورا وإظهار أنه يوفر تحيزات متوقعة بموجب افتراضات مناسبة حول توزيعات المعرفة المعجمية والعالمية. علاوة على ذلك، نلاحظ أن إخراج النموذج قوي للتغيرات في معاييرها داخل نطاقات معقولة.
Formal semantics in the Montagovian tradition provides precise meaning characterisations, but usually without a formal theory of the pragmatics of contextual parameters and their sensitivity to background knowledge. Meanwhile, formal pragmatic theories make explicit predictions about meaning in context, but generally without a well-defined compositional semantics. We propose a combined framework for the semantic and pragmatic interpretation of sentences in the face of probabilistic knowledge. We do so by (1) extending a Montagovian interpretation scheme to generate a distribution over possible meanings, and (2) generating a posterior for this distribution using a variant of the Rational Speech Act (RSA) models, but generalised to arbitrary propositions. These aspects of our framework are tied together by evaluating entailment under probabilistic uncertainty. We apply our model to anaphora resolution and show that it provides expected biases under suitable assumptions about the distributions of lexical and world-knowledge. Further, we observe that the model's output is robust to variations in its parameters within reasonable ranges.
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