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Register a new userArgumentation-Driven Evidence Association in Criminal Cases
جمعية الأدلة التي يحركها الحجة في القضايا الجنائية
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إن جمعية الأدلة في القضايا الجنائية تقسم مجموعة من الأدلة القضائية في العديد من مجموعات فرعية غير متداخلة، وتحسين إمكانية الترجمة الشفوية والشرعية في الإدانة.مريح، أدلة مقسمة إلى نفس المجموعة الفرعية عادة ما تدعم نفس المطالبة.لذلك، نقترح طريقة تعليمية مزدوجة مدفوعة بالحجة لحساب المسافة بين أزواج الأدلة لخطوة نقابة الأدلة التالية في هذه الورقة.النتائج التجريبية على مجموعة بيانات عالمية حقيقية توضح فعالية طريقتنا.
Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.
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In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of cheese pizza'' (a
menu item) and oreo cookies'' (a topping) from an input utterance Can I order a cheese pizza with oreo cookies on top?'' exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements.
المسؤولية الجنائية للذكاء الاصطناعي
تتمثل أهمية هذه الدراسة في أهمية موضوعها الجديد والحيوي، وهو المسؤولية الجنائية الناتجة عن أخطاء الذكاء الاصطناعي في التشريع الإماراتي "دراسة مقارنة"، فعلى امتداد الخمسين سنة الماضية تضافرت الجهود العالمية في عدد
من الميادين، كالفلسفة والقانون وعلم النفس وعلم المنطق والرياضيات، وعلم الأحياء وغيرها من العلوم، ومنذ سنوات بدأت هذه الجهود تحصد من ثمارها وظهرت إلى الوجود تطبيقات مذهلة للذكاء الاصطناعي، وهذا ما دفع دولة الإمارات العربية المتحدة لاستحداث وزارة للذكاء الاصطناعي وعلوم المستقبل، فهذه الخطوة تُضاف إلى سجل الإمارات الحافل بكل ما هو جديد في الثقافة والعلوم وغيرها من المجالات، فالإمارات سبّاقة في البحث وجلب أي أفكار جديدة أو عالمية وتطبيقها، والهدف من ذلك هو الارتقاء بالعمل الإداري. لأن اعتماد الإدارة على الذكاء الاصطناعي يساعدها على التكيف مع التغيرات المتلاحقة، ويساعدها أيضاً على مواجهة التحديات المتعددة والمختلفة، وبالتالي تحقيق الميزة التنافسية التي تسعى الإدارة إلى تحقيقها.
Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim. Previous works evaluate the entailment step based on the retrieved evidence, whereas we hypothesize that
the entailment prediction can provide useful signals for evidence retrieval, in the sense that if a sentence supports or refutes a claim, the sentence must be relevant. We propose a novel model that uses the entailment score to express the relevancy. Our experiments verify that leveraging entailment prediction improves ranking multiple pieces of evidence.
Appeared movements defending the rights of the consumer as a result of negligence
and shortcomings in for consumer rights, which include protecting consumers from
fraudulent practices and deception marketing and exploitation of its need for goods
and services, and the damage and the risk of physical, moral, suffered by the
consumer paid to the emergence of movements of consumer protection, came the
movement to protect consumer to act as consumer awareness and protection from
fraud and deception and neglect catalog. And represents a consumer protection
associations social action organized by the consumer, in order to embody the right to
listen to these consumers, and ensure the recovery of their rights that have been
damage by the other parties (producers, marketers, distributors) in the process of
exchange, causing them to lack of satisfaction to their needs and desires.
Was reached by searching mainly to the existence of fundamental differences with
significant between the demographic variables of the sample and the process of
creating awareness among consumers. As to reach a significant influence
statistically significant between the role of consumer protection association and the
process of creating awareness among consumers.
Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities. A plethora of solutions have been developed, but methods which leverage both structured and unstructured information to dete
ct misinformation are of particular relevance. In this paper, we tackle the FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) challenge which consists of an open source baseline system together with a benchmark dataset containing 87,026 verified claims. We extend this baseline model by improving the evidence retrieval module yielding the best evidence F1 score among the competitors in the challenge leaderboard while obtaining an overall FEVEROUS score of 0.20 (5th best ranked system).
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