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SD-QA: تحدث سؤالي من اللهاج المنطوق عن العالم الحقيقي
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تتوفر أنظمة الإجابة على الأسئلة (QA) الآن من خلال العديد من التطبيقات التجارية لمجموعة واسعة من المجالات، مما يخدم ملايين المستخدمين الذين يتفاعلون معهم عبر واجهات الكلام.ومع ذلك، فإن المعايير الحالية في أبحاث ضمنيا لا تحسب الأخطاء التي قد تعرضها نماذج التعرف على الكلام، ولا تفكر في اختلافات اللغة (لهجات) للمستخدمين.لمعالجة هذه الفجوة، نزيد من مجموعة بيانات QA الحالية لبناء معيارا متعدد الهياكل المتعددة، معيار QA المنطوقة في خمس لغات (العربية، البنغالية، الإنجليزية، الكورية، الكورية) مع أكثر من 68K Audio مطالبات في 24 لهجة من 255 متحدثا.نحن نقدم نتائج خط الأساس عرض الأداء العالمي الحقيقي لأنظمة ضمان الجودة وتحليل تأثير مجموعة متنوعة اللغات وغيرها من سمات المتكلم الحساسة على أداء المصب.أخيرا، ندرس عدالة نماذج ASR و QA فيما يتعلق بسكان المستخدمين الأساسيين.
Question answering (QA) systems are now available through numerous commercial applications for a wide variety of domains, serving millions of users that interact with them via speech interfaces. However, current benchmarks in QA research do not account for the errors that speech recognition models might introduce, nor do they consider the language variations (dialects) of the users. To address this gap, we augment an existing QA dataset to construct a multi-dialect, spoken QA benchmark on five languages (Arabic, Bengali, English, Kiswahili, Korean) with more than 68k audio prompts in 24 dialects from 255 speakers. We provide baseline results showcasing the real-world performance of QA systems and analyze the effect of language variety and other sensitive speaker attributes on downstream performance. Last, we study the fairness of the ASR and QA models with respect to the underlying user populations.
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