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Register a new userWord Representation Learning in Multimodal Pre-Trained Transformers: An Intrinsic Evaluation
تمثيل كلمة التعلم في المحولات متعددة المدربين مسبقا: تقييم جوهري
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مجردة أن هذه الدراسة تنفذ تقييم جوهري منهجي للتمثيل الدلالي الذي تعلمته المحولات متعددة الوسائط المدربة مسبقا. يزعم هذه التمثيلات أنها غير ملائمة للمهمة وأظهرت للمساعدة في العديد من مهام اللغة والرؤية المصب. ومع ذلك، فإن المدى الذي يتماشى فيه مع الحدس الدلالي البشري لا يزال غير واضح. نقوم بتجربة نماذج مختلفة والحصول على تمثيلات كلمة ثابتة من تلك السياق التي يتعلمونها. ثم قمنا بتقييمها ضد الأحكام الدلالية التي قدمها مكبرات الصوت البشرية. تمشيا مع الأدلة السابقة، نلاحظ ميزة معممة للتمثيلات متعددة الوسائط على اللغات فقط على أزواج كلمة ملموسة، ولكن ليس على تلك المجردة. من ناحية، يؤكد ذلك فعالية هذه النماذج لمحاذاة اللغة والرؤية، مما يؤدي إلى تحسين تمثيلات الدلالية للمفاهيم التي ترتكز في الصور. من ناحية أخرى، تبين أن النماذج تتبع أنماط تعليم التمثيل المختلفة، والتي سفي بعض الضوء على كيفية وعند تنفيذ تكامل متعدد الوسائط.
Abstract This study carries out a systematic intrinsic evaluation of the semantic representations learned by state-of-the-art pre-trained multimodal Transformers. These representations are claimed to be task-agnostic and shown to help on many downstream language-and-vision tasks. However, the extent to which they align with human semantic intuitions remains unclear. We experiment with various models and obtain static word representations from the contextualized ones they learn. We then evaluate them against the semantic judgments provided by human speakers. In line with previous evidence, we observe a generalized advantage of multimodal representations over language- only ones on concrete word pairs, but not on abstract ones. On the one hand, this confirms the effectiveness of these models to align language and vision, which results in better semantic representations for concepts that are grounded in images. On the other hand, models are shown to follow different representation learning patterns, which sheds some light on how and when they perform multimodal integration.
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