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Register a new userMr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
السيد تاي: معيار متعدد اللغات لاسترجاع كثيف
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نقدم السيد Tydi، وهي مجموعة بيانات مرجعية متعددة اللغات لاسترجاع أحادي اللغات في أحد عشر لغة متنوعة من الناحية النموذجية، مصممة لتقييم الترتيب مع التمثيلات الكثيفة المستفادة.الهدف من هذا المورد هو أن يحفز البحث في تقنيات استرجاع كثيفة باللغات غير الإنجليزية، بدافع من الملاحظات الحديثة أن التقنيات الحالية لتعلم التمثيل تؤدي سيئة عند تطبيقها على بيانات خارج التوزيع.كنقطة انطلاق، نحن نقدم خطوط خطوط خطوط البيانات الخاصة بهذه البيانات الجديدة القائمة على التكيف متعدد اللغات من DPR التي نسميها MDPR ".تبين التجارب أنه على الرغم من أن فعالية MDPR أقل بكثير من BM25، إلا أن تمثيلات كثيفة يبدو أنها توفر إشارات ذات أهمية قيمة، وتحسين نتائج BM25 في Sparse - الهجينة الكثيفة.بالإضافة إلى تحليلات نتائجنا، نناقش أيضا التحديات المستقبلية وتقديم جدول أعمال بحث في استرجاع كثيف متعدد اللغات.يمكن تنزيل السيد Tydi في https://github.com/castorini/mr.tydi.
We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations. The goal of this resource is to spur research in dense retrieval techniques in non-English languages, motivated by recent observations that existing techniques for representation learning perform poorly when applied to out-of-distribution data. As a starting point, we provide zero-shot baselines for this new dataset based on a multi-lingual adaptation of DPR that we call mDPR''. Experiments show that although the effectiveness of mDPR is much lower than BM25, dense representations nevertheless appear to provide valuable relevance signals, improving BM25 results in sparse--dense hybrids. In addition to analyses of our results, we also discuss future challenges and present a research agenda in multi-lingual dense retrieval. Mr. TyDi can be downloaded at https://github.com/castorini/mr.tydi.
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