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اللغة في المربع (البحث): تعلم لغة التأريض في تفاعل العالم الحقيقي
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نحن نحقق في تعلم اللغة الأساسية من خلال بيانات عالمية حقيقية، من خلال نمذجة ديناميات متعلم المعلم من خلال التفاعلات الطبيعية التي تحدث بين المستخدمين ومحركات البحث؛على وجه الخصوص، نستكشف ظهور التعميم الدلالي من تمثيلات كثيفة غير مخالفة خارج البيئات الاصطناعية.يتم تعلم مجال التأريض وظيفة دلالة ودالة تكوين من بيانات المستخدم فقط.نظهر كيف تظهر الدلالات الناتجة عن عبارات الاسم خصائصا تتراكم بينما تكون مائيا تماما دون أي وضع علامات واضحة.نحن نقسم لدينا دلالاتنا المتطرفة على التركيبية ومهام الاستدلال صفرية، ونرى أنها توفر نتائج أفضل وتعميمات أفضل من نماذج SOTA غير المدرجة، مثل Word2VEC و BERT.
We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines; in particular, we explore the emergence of semantic generalization from unsupervised dense representations outside of synthetic environments. A grounding domain, a denotation function and a composition function are learned from user data only. We show how the resulting semantics for noun phrases exhibits compositional properties while being fully learnable without any explicit labelling. We benchmark our grounded semantics on compositionality and zero-shot inference tasks, and we show that it provides better results and better generalizations than SOTA non-grounded models, such as word2vec and BERT.
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