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Register a new userThe legal rules governing transferable letter of credit
الأحكام القانونية الناظمة للاعتمادات المستندية القابلة للتحويل
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Aِl-Baath University ورقة بحثية
Publication date
2018
fields
Law
and research's language is
العربية
Authors
ميسون عبد الوهاب المصري( باحث )
Created by
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من خلال هذا البحث سندرس الأحكام القانونية الناظمة للاعتمادات المستندية القابمة للتحويل وفقا للمخطط التالي : الفصل الأول: شروط الاعتمادات المستندية القابلة للتحويل. الفصل الثاني: آلية عمل الاعتمادات المستندية القابلة للتحويل.
Throughout this research we will study the legal rules governing the transferable letter of credit according to the following plan: The first chapter: The items of transferable letter of credit. The second chapter: The mechanism of transferable letter of credit.
References used
da. alhusayn, husayn shahada, 2007- altashrieat almasrifia, manshurat jamieat hulb, kuliyat alhuquq, s 277
alsieayd, samah yusif 'iismaeil, 2007- alealaqa altaeaqudia bayn 'atraf eaqd alaietimad almustandi, jamieat alnajah alwataniati, kuliyat aldirasat aleulya , filastin, nabulus, 167 safhat
alsiysiiy, salah aldiyn husun, 2004- qadaya masrafiat mueasirat, alaitiman almusrifiu, aldamanat almasrafiatu, alaietimadat almustandiatu. altibeat al'uwalaa, dar alfikr alearabiu, alqahiratu, 310safhat.an
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Since the documentary credits were the most widely used in the
field of international commerce, as to ensure the execution of
international sales contracts, the question, of the legal nature of the
two categories of documentary credits : Simple (w
hich did not
stipulate to be transferable) and the transferable one, is extremely
important.
To illustrate the special nature of documentary credit, it is
necessary to differentiate between the documentary credit simple
and other similar operations: as letter of guarantee, standby Credit
and documentary collection, and between the transferable
documentary credit and the other similar operations as assignment
of right, back to back credit and other specific credits.
This study aims to shed the light on the threats that facing the
commercial banks dealing with the Documentary Credits, and the
affect of the ICC Uniform Customs and Practice for Documentary
Credits (UCP 600) in reducing these threats.
The unification of legal rules of the international trade has been achieved by
different means. The academics and the concerned people have been very actives in
introducing some legal and practical propositions to unify the rule of international
t
rade. The different proposition of the academics transferred to became
international or regional treaties, international customs and practices and / or model
contracts issued by international organizations (such as the Unified Rule for
Documentary Credit that were issued by International Chamber of Commerce and
the Joint "Venture" model agreement the was issued by (UNCITRA).
While neural networks produce state-of-the-art performance in several NLP tasks, they generally depend heavily on lexicalized information, which transfer poorly between domains. We present a combination of two strategies to mitigate this dependence o
n lexicalized information in fact verification tasks. We present a data distillation technique for delexicalization, which we then combine with a model distillation method to prevent aggressive data distillation. We show that by using our solution, not only does the performance of an existing state-of-the-art model remain at par with that of the model trained on a fully lexicalized data, but it also performs better than it when tested out of domain. We show that the technique we present encourages models to extract transferable facts from a given fact verification dataset.
Weakly-supervised table question-answering (TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table to produce structured query for the question. However, in practical se
ttings TableQA systems are deployed over table corpora having topic and word distributions quite distinct from BERT's pretraining corpus. In this work we simulate the practical topic shift scenario by designing novel challenge benchmarks WikiSQL-TS and WikiTable-TS, consisting of train-dev-test splits in five distinct topic groups, based on the popular WikiSQL and WikiTable-Questions datasets. We empirically show that, despite pre-training on large open-domain text, performance of models degrades significantly when they are evaluated on unseen topics. In response, we propose T3QA (Topic Transferable Table Question Answering) a pragmatic adaptation framework for TableQA comprising of: (1) topic-specific vocabulary injection into BERT, (2) a novel text-to-text transformer generator (such as T5, GPT2) based natural language question generation pipeline focused on generating topic-specific training data, and (3) a logical form re-ranker. We show that T3QA provides a reasonably good baseline for our topic shift benchmarks. We believe our topic split benchmarks will lead to robust TableQA solutions that are better suited for practical deployment
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