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Register a new userCOIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
لفائف: إعادة النظر في المباراة المعجمية الدقيقة في استرجاع المعلومات مع القائمة المقلوبة من السياق
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تعتمد أنظمة استرجاع المعلومات الكلاسيكية مثل BM25 على المباراة المعجمية الدقيقة ويمكنها تنفيذ البحث بكفاءة مع فهرس قائمة مقلوب.تتحول نماذج الأشعة تحت الحديثة الأخيرة نحو ناعم مطابقة شروط مستندات الاستعلام، لكنها تفقد كفاءة حساب أنظمة المطابقة الدقيقة.تقدم هذه الورقة لفائف، وهي عبارة عن بنية استرجاع مطابقة محددة في السياق، حيث تعتمد التهديف على تمثيلات محكومتي استفسارات الاستعلام المتداخلة.تخزن الهندسة المعمارية الجديدة تمثيلات الرمز المميز للسيتق في القوائم المقلوبة، مما يوفر كفاءة المباراة الدقيقة وقوة التمثيل لنماذج اللغة العميقة.تظهر النتائج التجريبية لدينا لفائف تفوق المستردات المعجمية الكلاسيكية واسترجاع LM العميق الحديثة مع الكمون مماثل أو أصغر.
Classical information retrieval systems such as BM25 rely on exact lexical match and can carry out search efficiently with inverted list index. Recent neural IR models shifts towards soft matching all query document terms, but they lose the computation efficiency of exact match systems. This paper presents COIL, a contextualized exact match retrieval architecture, where scoring is based on overlapping query document tokens' contextualized representations. The new architecture stores contextualized token representations in inverted lists, bringing together the efficiency of exact match and the representation power of deep language models. Our experimental results show COIL outperforms classical lexical retrievers and state-of-the-art deep LM retrievers with similar or smaller latency.
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