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MIMOQA: مساهمة متعددة الوسائط متعددة الوسائط
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اختارت الأبحاث متعددة الوسائط بشكل كبير في مساحة السؤال الرد على المهمة التي يتم تمديدها إلى السؤال المرئي الرد على الرسوم البيانية، والرسوم البيانية الإجابة عليها وكذلك مسألة مساهمة مدخل متعددة الوسائط.ومع ذلك، فإن كل هذه الاستكشافات تنتج إخراج نصي غير مهني كإجابة.في هذه الورقة، نقترح مهمة رواية - MIMOQA - الإدخال المتعدد الوسائط المتعددة الناتج السؤال الرد الذي يكون فيه الإخراج متعدد الوسائط.من خلال التجارب البشرية، نوضح تجريبيا أن هذه النواتج متعددة الوسائط توفر فهما معرفيا أفضل للإجابات.نقترح أيضا إطارا للردا على السؤال متعدد الوسائط، ميكسبرت، يشتمل على اهتماما نصي مشتركا ومرفقيا نحو إنتاج مثل هذا الناتج متعدد الوسائط.تعتمد طريقنا على مجموعة بيانات متعددة الوسائط غير مصنفة لهذه المشكلة من مجموعات البيانات غير المتوفرة للجمهور.نظهر الأداء الفائق ل Mexbert ضد خطوط أساسية قوية على كل من المقاييس التلقائية وكذلك الإنسان.
Multimodal research has picked up significantly in the space of question answering with the task being extended to visual question answering, charts question answering as well as multimodal input question answering. However, all these explorations produce a unimodal textual output as the answer. In this paper, we propose a novel task - MIMOQA - Multimodal Input Multimodal Output Question Answering in which the output is also multimodal. Through human experiments, we empirically show that such multimodal outputs provide better cognitive understanding of the answers. We also propose a novel multimodal question-answering framework, MExBERT, that incorporates a joint textual and visual attention towards producing such a multimodal output. Our method relies on a novel multimodal dataset curated for this problem from publicly available unimodal datasets. We show the superior performance of MExBERT against strong baselines on both the automatic as well as human metrics.
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