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Register a new userLightweight Models for Multimodal Sequential Data
نماذج خفيفة الوزن للبيانات المتسلسلة متعددة الوسائط
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تشمل اللغة البشرية أكثر من مجرد نص؛كما أنه ينقل العواطف من خلال النغمة والإيماءات.نقدم دراسة حالة لثلاث هندسة بسيطة وفعالة قائمة على المحولات لتنبؤ المعنويات والعاطفة في البيانات متعددة الوسائط.يقوم نموذج الانصهار المتأخر بدمج ميزات Unimodal لإنشاء تسلسل ميزة متعددة الوسائط، يجمع نموذج Robin Robin بشكل متكرر بين ميزات BIMODAL باستخدام اهتماما عبر الوسائط، ويجمع نموذج الانصهار الهجين بين ميزات Trimodal و Unimodal معا لتشكيل تسلسل مائع نهائي للتنبؤ بالمشاعر.تبين تجاربنا أن نماذجنا الصغيرة فعالة ومتفوقة على الإصدارات التي تم إصدارها علنا من أنظمة تحليل المعنويات متعددة الوسائط الأكبر والحديثة.
Human language encompasses more than just text; it also conveys emotions through tone and gestures. We present a case study of three simple and efficient Transformer-based architectures for predicting sentiment and emotion in multimodal data. The Late Fusion model merges unimodal features to create a multimodal feature sequence, the Round Robin model iteratively combines bimodal features using cross-modal attention, and the Hybrid Fusion model combines trimodal and unimodal features together to form a final feature sequence for predicting sentiment. Our experiments show that our small models are effective and outperform the publicly released versions of much larger, state-of-the-art multimodal sentiment analysis systems.
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