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Register a new userCan images help recognize entities? A study of the role of images for Multimodal NER
يمكن أن تساعد الصور على التعرف على الكيانات؟دراسة دور الصور لعدة متعددة
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يتطلب التعرف على الكيان المسمى MultiModal (MNER) سد الفجوة بين فهم اللغة والسياق المرئي.في حين أن العديد من التقنيات العصبية متعددة الوسائط قد تم اقتراح دمج الصور في مهمة MNER، فإن قدرة النموذج على الاستفادة من التفاعلات متعددة الوسائط لا تزال مفهومة سيئة.في هذا العمل، نقوم بإجراء تحليلات متعمقة من تقنيات الانصهار متعددة الوسائط المتعددة من وجهات نظر مختلفة ووصف السيناريوهات حيث لا تؤدي إضافة معلومات من الصورة دائما إلى زيادة الأداء.ندرس أيضا استخدام التسميات التوضيحية كوسيلة لإثراء السياق ل MNER.تعرض التجارب في ثلاث مجموعات من المنصات الاجتماعية الشعبية عنق الزجاجة من النماذج متعددة الوسائط الحالية والحالات التي يستخدمها المساميرات مفيدة.
Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context. While many multimodal neural techniques have been proposed to incorporate images into the MNER task, the model's ability to leverage multimodal interactions remains poorly understood. In this work, we conduct in-depth analyses of existing multimodal fusion techniques from different perspectives and describe the scenarios where adding information from the image does not always boost performance. We also study the use of captions as a way to enrich the context for MNER. Experiments on three datasets from popular social platforms expose the bottleneck of existing multimodal models and the situations where using captions is beneficial.
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