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Register a new userالمجلس العالي لتفسير الدستور هل هو محكمة دستورية؟
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نفيس صالح مدانات( باحث )
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خصص هذا البحث لإثبات أن هذا المجلس هو محكمة دستورية لها خصائص المحاكم الدستورية الأوروبية. فالمجس العالي يوجد في بلد هي المملكة الأردنية الهاشمية تعتمد على النظام البرلماني، كما تعتمد نظام تعدد القضاء، و له نظام قانوني دستوري يبين تشكيله و سيره و اختصاصاته، كما أنه يحتكر النزاع الدستوري حسب نص الدستور، إلى جانب أنه قضاء حقيقي خارج الأداة القضائية، و له الصلاحية المركزية العمومية.
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References used
الجرف – الدكتور طعيمة الجرف، "موجز القانون الدستوري"، القاهرة، 1960
الحياري – الدكتور عادل الحياري، "القانون الدستوري والنظام الدستوري الأردني"، دراسة مقارنة، طبعة أولى، 1972
السيد – الدكتور السيد صبري، "القانون الدستوري"، 1949
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Research in NLP has mainly focused on factoid questions, with the goal of finding quick and reliable ways of matching a query to an answer. However, human discourse involves more than that: it contains non-canonical questions deployed to achieve spec
ific communicative goals. In this paper, we investigate this under-studied aspect of NLP by introducing a targeted task, creating an appropriate corpus for the task and providing baseline models of diverse nature. With this, we are also able to generate useful insights on the task and open the way for future research in this direction.
Contrastive explanations clarify why an event occurred in contrast to another. They are inherently intuitive to humans to both produce and comprehend. We propose a method to produce contrastive explanations in the latent space, via a projection of th
e input representation, such that only the features that differentiate two potential decisions are captured. Our modification allows model behavior to consider only contrastive reasoning, and uncover which aspects of the input are useful for and against particular decisions. Our contrastive explanations can additionally answer for which label, and against which alternative label, is a given input feature useful. We produce contrastive explanations via both high-level abstract concept attribution and low-level input token/span attribution for two NLP classification benchmarks. Our findings demonstrate the ability of label-contrastive explanations to provide fine-grained interpretability of model decisions.
تعدُّ الرقابة على دستورية القوانين الوسيلة الأنجع لضمان احترام الدستور وسموه على بقية القواعد القانونية. هذه الرقابة يمكن أن تكون رقابة سياسية عندما يعهد بها إلى هيئة سياسية، أو رقابة قضائية عندما تتولاها هيئة ذات طابع قضائي. إن الرقابة القضائية يمكن
أن تكون سابقة على إصدار القانون كما يمكن أن تكون لاحقة على إصداره.
و تمارس هذه الرقابة عن طريق الدعوى الأصلية بالإلغاء، كما يمكن ممارستها بالدفع بعدم الدستورية عن طريق الامتناع.
Commonsense inference to understand and explain human language is a fundamental research problem in natural language processing. Explaining human conversations poses a great challenge as it requires contextual understanding, planning, inference, and
several aspects of reasoning including causal, temporal, and commonsense reasoning. In this work, we introduce CIDER -- a manually curated dataset that contains dyadic dialogue explanations in the form of implicit and explicit knowledge triplets inferred using contextual commonsense inference. Extracting such rich explanations from conversations can be conducive to improving several downstream applications. The annotated triplets are categorized by the type of commonsense knowledge present (e.g., causal, conditional, temporal). We set up three different tasks conditioned on the annotated dataset: Dialogue-level Natural Language Inference, Span Extraction, and Multi-choice Span Selection. Baseline results obtained with transformer-based models reveal that the tasks are difficult, paving the way for promising future research. The dataset and the baseline implementations are publicly available at https://github.com/declare-lab/CIDER.
The last years have shown rapid developments in the field of multimodal machine learning, combining e.g., vision, text or speech. In this position paper we explain how the field uses outdated definitions of multimodality that prove unfit for the mach
ine learning era. We propose a new task-relative definition of (multi)modality in the context of multimodal machine learning that focuses on representations and information that are relevant for a given machine learning task. With our new definition of multimodality we aim to provide a missing foundation for multimodal research, an important component of language grounding and a crucial milestone towards NLU.
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