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Register a new userAdaptive Information Seeking for Open-Domain Question Answering
معلومات تكيف تسعى للحصول على سؤال مفتوح
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تعد المعلومات التي تطلبها خطوة أساسية للسؤال المفتوح الإجابة على جمع الأدلة الكفاءة من كوربوس كبيرة. في الآونة الأخيرة، أثبتت النهج التكرارية أن تكون فعالة للأسئلة المعقدة، من خلال استرداد أدلة جديدة بشكل متكرر في كل خطوة. ومع ذلك، فإن جميع الأساليب التكرارية الحالية تقريبا تستخدم استراتيجيات محددة مسبقا، إما تطبيق نفس وظيفة الاسترجاع عدة مرات أو إصلاح ترتيب وظائف استرجاع مختلفة، والتي لا يمكنها الوفاء بالمتطلبات المتنوعة من الأسئلة المختلفة. في هذه الورقة، نقترح استراتيجية رواية تكيفية تسعى للحصول على معلومات عن أسئلة مفتوحة، وهي AISO. على وجه التحديد، يتم تصميم عملية الاسترجاع والأجوبة بأكملها كعملية اتخاذ قرار Markov الملحوظ جزئيا، حيث يتم تعريف ثلاثة أنواع من عمليات استرجاع (مثل E.G.، BM25 و DPR وارتباط التشعبي) وعملية إجابة واحدة كإجراءات. وفقا للسياسة المستفادة، يمكن ل AISO اختيار إجراءات استرجاع مناسبة ستكيفا للبحث عن الأدلة المفقودة في كل خطوة، بناء على الأدلة التي تم جمعها واستفسلة إعادة صياغة، أو إخراج الإجابة مباشرة عندما تكون مجموعة الأدلة كافية للسؤال. تبين تجارب في تشكيلة مفتوحة و hotpotqa fullwiki، التي تخدم مع معايير قافلة واحدة مفتوحة ومتعددة النطاق، أن AISO تفوقت على جميع الأساليب الأساسية مع استراتيجيات محددة مسبقا فيما يتعلق بتقييمات الاسترجاع والإجابة.
Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus. Recently, iterative approaches have been proven to be effective for complex questions, by recursively retrieving new evidence at each step. However, almost all existing iterative approaches use predefined strategies, either applying the same retrieval function multiple times or fixing the order of different retrieval functions, which cannot fulfill the diverse requirements of various questions. In this paper, we propose a novel adaptive information-seeking strategy for open-domain question answering, namely AISO. Specifically, the whole retrieval and answer process is modeled as a partially observed Markov decision process, where three types of retrieval operations (e.g., BM25, DPR, and hyperlink) and one answer operation are defined as actions. According to the learned policy, AISO could adaptively select a proper retrieval action to seek the missing evidence at each step, based on the collected evidence and the reformulated query, or directly output the answer when the evidence set is sufficient for the question. Experiments on SQuAD Open and HotpotQA fullwiki, which serve as single-hop and multi-hop open-domain QA benchmarks, show that AISO outperforms all baseline methods with predefined strategies in terms of both retrieval and answer evaluations.
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