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XOR QA: الإجابة على سؤال سؤال مفتوح
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تفترض السؤال المتعدد اللغات الرد على المهام عادة أن الإجابات موجودة بنفس اللغة مثل السؤال. ومع ذلك، في الممارسة العملية، تواجه العديد من اللغات كل من ندرة المعلومات --- حيث تحتوي اللغات على عدد قليل من المقالات المرجعية --- واستاجةم المعلومات --- أين الأسئلة المرجعية المفاهيم من الثقافات الأخرى. يمتد هذا العمل سؤالا مفتوحا للاسترجاع الرد على الإعداد المتبادل الذي تمكن الأسئلة من لغة واحدة للإجابة على محتوى الإجابة من لغة أخرى. نحن نبني مجموعة بيانات واسعة النطاق تم بناؤها على أسئلة 40K تسعى للحصول على معلومات عبر 7 لغات غير الإنجليزية متنوعة لا يمكن أن تجد Tydi QA إجابات لغة نفسها. استنادا إلى هذه البيانات، نقدم إطار عمل، يسمى سؤالا عبر اللغات المفتوح استرجاع الإجابة (XOR QA)، الذي يتكون من ثلاث مهام جديدة تنطوي على استرجاع وثائق عبر اللغات من موارد متعددة اللغات والإنجليزية. نقوم بإنشاء خطوط الأساس مع أنظمة ترجمة من الآلة الحديثة ونماذج مسببة الاحتياطية عبر اللغات. تشير النتائج التجريبية إلى أن XOR QA هي مهمة صعبة سيسهل تطوير تقنيات جديدة للإجابة على الأسئلة متعددة اللغات. تتوفر بياناتنا ورمزنا في https://nlp.cs.washington.edu/xorqa/.
Multilingual question answering tasks typically assume that answers exist in the same language as the question. Yet in practice, many languages face both information scarcity---where languages have few reference articles---and information asymmetry---where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on 40K information-seeking questions across 7 diverse non-English languages that TyDi QA could not find same-language answers for. Based on this dataset, we introduce a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving cross-lingual document retrieval from multilingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available at https://nlp.cs.washington.edu/xorqa/.
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