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Register a new userDealing with Typos for BERT-based Passage Retrieval and Ranking
التعامل مع الأخطاء المطبعية لاسترجاع المقطع المستند إلى بيرت
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استرجاع المرور والترتيب هو مهمة رئيسية في الإجابة على الأسئلة المفتوحة واسترجاع المعلومات. تعتمد الأساليب الفعالة الحالية في الغالب على المستردين المعتمدين على النموذج العميق المدربين مسبقا. تم عرض هذه الأساليب نموذجا بفعالية المطابقة الدلالية بين الاستعلامات والمرورات، أيضا في وجود عدم تطابق الكلمات الرئيسية، أي الممرات ذات الصلة بالاستعلام ولكن لا تحتوي على كلمات رئيسية مهمة. في هذه الورقة، نعتبر المسترد الكثيف (DR) وطريقة استرجاع الممر، و Re-Ranker Reveer، وهي طريقة إعادة ترتيب الشعبية. في هذا السياق، نحقق رسميا كيفية استجابة هذه النماذج والتكيف مع نوع معين من عدم تطابق الكلمة الرئيسية - التي تحدث عن طريق الكلمة الأساسية التي تحدث في استفسارات. من خلال التحقيق التجريبي، نجد أن الأخطاء المطبعية يمكن أن تؤدي إلى انخفاض كبير في فعالية الاسترجاع والترتيب. بعد ذلك اقترحنا إطارا بسيطا للتدريب في الطباعة المطبعية عن DR و Bert Re-Ranker لمعالجة هذه المسألة. نظرا لنتائجنا التجريبية على مجموعة بيانات مرتبة مرور MS MARCO، بإظهار مجموعة بيانات MS MARCO، من خلال التدريب على الأخطاء المطبعية المقترحة، يمكن أن يصبح تدريب DR و Bert Re-Ranker قويا للمخططات المطبعية في الاستفسارات، مما يؤدي إلى تحسين فعالية محسنة بشكل كبير مقارنة بالنماذج المدربة دون محاسبة بشكل مناسب عن الأخطاء المطبعية.
Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers. These methods have been shown to effectively model the semantic matching between queries and passages, also in presence of keyword mismatch, i.e. passages that are relevant to a query but do not contain important query keywords. In this paper we consider the Dense Retriever (DR), a passage retrieval method, and the BERT re-ranker, a popular passage re-ranking method. In this context, we formally investigate how these models respond and adapt to a specific type of keyword mismatch -- that caused by keyword typos occurring in queries. Through empirical investigation, we find that typos can lead to a significant drop in retrieval and ranking effectiveness. We then propose a simple typos-aware training framework for DR and BERT re-ranker to address this issue. Our experimental results on the MS MARCO passage ranking dataset show that, with our proposed typos-aware training, DR and BERT re-ranker can become robust to typos in queries, resulting in significantly improved effectiveness compared to models trained without appropriately accounting for typos.
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