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فريق Papelo في FEVEROUS: الأدلة متعددة القفز
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نقوم بتطوير نظام لمهمة استخراج الحقائق الحميرة والتحقق من تحديد مجموعة أولية من الأدلة المحتملة، ثم يتابع الأدلة المفقودة في القفزات اللاحقة من خلال محاولة توليدها، مع وجود وحدة توقعات القفز التالية "التي يتم مطابقة خرجها من عناصر الصفحاتمقال متوقع.تسعى للحصول على أدلة مع وحدة تنبؤ القفز التالية تستمر في تحسين النتيجة الحميرة لمدة تصل إلى سبع قفزات.يتم تدريب تصنيف العلامات على سلاسل الأدلة المستخرجة غير كاملة غير كاملة، واستخدام تلميحات التي تسهل المقارنة العددية.يحقق النظام .281 النتيجة الحميرة ودقة التسمية .658 على مجموعة التطوير، وينتهي في المرتبة الثانية باستخدام 0.259 درجة حمامة ودقة التسمية .576 على مجموعة الاختبار.
We develop a system for the FEVEROUS fact extraction and verification task that ranks an initial set of potential evidence and then pursues missing evidence in subsequent hops by trying to generate it, with a next hop prediction module'' whose output is matched against page elements in a predicted article. Seeking evidence with the next hop prediction module continues to improve FEVEROUS score for up to seven hops. Label classification is trained on possibly incomplete extracted evidence chains, utilizing hints that facilitate numerical comparison. The system achieves .281 FEVEROUS score and .658 label accuracy on the development set, and finishes in second place with .259 FEVEROUS score and .576 label accuracy on the test set.
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This paper describes the winning system for TextGraphs 2021 shared task: Multi-hop inference explanation regeneration. Given a question and its corresponding correct answer, this task aims to select the facts that can explain why the answer is correc
t for that question and answering (QA) from a large knowledge base. To address this problem and accelerate training as well, our strategy includes two steps. First, fine-tuning pre-trained language models (PLMs) with triplet loss to recall top-K relevant facts for each question and answer pair. Then, adopting the same architecture to train the re-ranking model to rank the top-K candidates. To further improve the performance, we average the results from models based on different PLMs (e.g., RoBERTa) and different parameter settings to make the final predictions. The official evaluation shows that, our system can outperform the second best system by 4.93 points, which proves the effectiveness of our system. Our code has been open source, address is https://github.com/DeepBlueAI/TextGraphs-15
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