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مسألة متعددة المجالات يجيب
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الإجابة السؤالية (QA) هي واحدة من أكثر المهام التحدي والآثار في معالجة اللغة الطبيعية.ومع ذلك، ركزت معظم الأبحاث في ضمان الجودة على النطاق المفتوح أو الأبدية في حين أن معظم تطبيقات العالم الواقعي تعامل مع مجالات أو لغات محددة.في هذا البرنامج التعليمي، نحاول سد هذه الفجوة.أولا، نقدم معايير قياسية في مجال QA متعدد اللغات متعددة اللغات.في كل من السيناريوهين، نناقش النهج الحديثة التي تحقق أداء مثير للإعجاب، تتراوح من التعلم من تحويل صفرية إلى التدريب خارج الصندوق مع أنظمة QA المجال المفتوحة.أخيرا، سنقدم مشاكل بحثية مفتوحة أن أجندة الأبحاث الجديدة تشكل مثل التعلم متعدد المهام، وتعلم التحويل عبر اللغات، وتكييف المجال وتدريب نماذج لغة متعددة اللغات المدربة مسبقا مسبقا.
Question answering (QA) is one of the most challenging and impactful tasks in natural language processing. Most research in QA, however, has focused on the open-domain or monolingual setting while most real-world applications deal with specific domains or languages. In this tutorial, we attempt to bridge this gap. Firstly, we introduce standard benchmarks in multi-domain and multilingual QA. In both scenarios, we discuss state-of-the-art approaches that achieve impressive performance, ranging from zero-shot transfer learning to out-of-the-box training with open-domain QA systems. Finally, we will present open research problems that this new research agenda poses such as multi-task learning, cross-lingual transfer learning, domain adaptation and training large scale pre-trained multilingual language models.
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