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MKQA: معيار متنوع لغالي اللغوي للنطاق متعدد اللغات
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التقدم الملخص في النمذجة المتبادلة يعتمد على مجموعات التقييم الصعبة والواقعية والتنوع.نقدم أسئلة وأجوبة معارف متعددة اللغات (MKQA)، وهي سؤالا مفتوحا في مجال الإجابة على مجموعة التقييم التي تضم أزواج من الإجابات السؤال 10 كيلو محاذاة عبر 26 لغة متنوعة من الناحية النموذجية (أزواج الإجابة السؤال 260k في المجموع).تستند الإجابات إلى تمثيل بيانات غير مستقر بشدة، مما يجعل النتائج قابلة للمقارنة عبر اللغات والمستقل عن الممرات الخاصة باللغة.مع 26 لغة، توفر مجموعة البيانات هذه الأوسع نطاقا من اللغات حتى الآن لتقييم الإجابة على السؤال.نحن نقسم مجموعة متنوعة من الأساليب وخطوط الأساس للدولة والأساس للاستخراج الاستقبال، المدربين على الأسئلة الطبيعية، في صفر لقطة وإعدادات الترجمة.تشير النتائج إلى أن هذه البيانات تتحدى حتى باللغة الإنجليزية، ولكن خاصة في لغات الموارد المنخفضة
Abstract Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open- domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). Answers are based on heavily curated, language- independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. We benchmark a variety of state- of-the-art methods and baselines for generative and extractive question answering, trained on Natural Questions, in zero shot and translation settings. Results indicate this dataset is challenging even in English, but especially in low-resource languages.1
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Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum inner product search in the retrieval process. However, current dense
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