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Register a new userDoes Vision-and-Language Pretraining Improve Lexical Grounding?
هل تتم محاكاة الرؤية واللغة تحسين التأريض المعجمي؟
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تم انتقاد التمثيل اللغوي المستمدة من النص وحده بسبب نقص الأساس، أي ربط الكلمات مع معانيها في العالم المادي.عرضت نماذج الرؤية واللغة (VL)، التي تم تدريبها بالاشتراك على نص بيانات النص والصورة أو الفيديو كرددا على مثل هذه الانتقادات.ومع ذلك، في حين أظهرت مؤشر الأفلام VL النجاح على مهام متعددة الوسائط مثل الإجابة على السؤال المرئي، فإنه لم يعرف بعد كيف المقارنة بين التمثيلات اللغوية الداخلية أنفسهم بنظرائهم النصي فقط.تقارن هذه الورقة التمثيلات الدلالية المستفادة عبر VL مقابل النص لا يمكن أن تحذر فقط عن نماذج VL الأخيرة باستخدام مجموعة من التحليلات (التجميع والتحقيق والأداء في مهمة الإجابة على سؤال للبلد) في وضع لغة فقط.نجد أن النماذج متعددة الوسائط تفشل في الظهور بشكل كبير من المتغيرات النصية فقط، مما يشير إلى أن العمل المستقبلي مطلوب إذا تم اتباع الاحتجاج متعدد الوسائط بمثابة متابعته كوسيلة لتحسين NLP بشكل عام.
Linguistic representations derived from text alone have been criticized for their lack of grounding, i.e., connecting words to their meanings in the physical world. Vision-and- Language (VL) models, trained jointly on text and image or video data, have been offered as a response to such criticisms. However, while VL pretraining has shown success on multimodal tasks such as visual question answering, it is not yet known how the internal linguistic representations themselves compare to their text-only counterparts. This paper compares the semantic representations learned via VL vs. text-only pretraining for two recent VL models using a suite of analyses (clustering, probing, and performance on a commonsense question answering task) in a language-only setting. We find that the multimodal models fail to significantly outperform the text-only variants, suggesting that future work is required if multimodal pretraining is to be pursued as a means of improving NLP in general.
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