Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAutomatic Resolution of Domain Name Disputes
الدقة التلقائية لنزاعات اسم النطاق
218
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نقدم المهمة الجديدة لحل النزاعات اسم النطاق (DNDR)، والذي يتنبأ بنتيجة عملية لحل النزاعات حول استحقاق قانوني لاسم النطاق.يحدد TheICann UDRP عملية تحكيم إلزامية للنزاع بين مالك العلامة التجارية وسجل اسم النطاق المتعلق باسم مجال كبير المستوى (GTLD) (GTLD) (واحد ينتهي في .com، .org، .NET، إلخ).يؤدي طبيعة المشكلة إلى مجموعة بيانات متحفة للغاية، والتي تنبع من القدرة على تسجيل اسم مجال مع سهولة شديدة، ومصروفات قليلة للغاية، ولا حاجة لإثبات استحقاقها.في هذه الورقة، نحن تصف ثيتاسك ومجموعة البيانات المرتبطة بها.ونحن نقدم أيضا نتائج مرجعية بناء على مجموعة من MOD-ELS، والتي تظهر أن خطوط الأساس البسيطة بشكل عام صعبة التغلب عليها بسبب توزيع البيانات المنحرفة، ولكن في حالة المستفتى المحددة التي قدمت استجابة، برت ضبط صقليقدم النموذج تحسينات كبيرة على نموذج فئة الأغلبية
We introduce the new task of domain name dispute resolution (DNDR), that predicts the outcome of a process for resolving disputes about legal entitlement to a domain name. TheICANN UDRP establishes a mandatory arbitration process for a dispute between a trade-mark owner and a domain name registrant pertaining to a generic Top-Level Domain (gTLD) name (one ending in .COM, .ORG, .NET, etc). The nature of the problem leads to a very skewed data set, which stems from being able to register a domain name with extreme ease, very little expense, and no need to prove an entitlement to it. In this paper, we describe thetask and associated data set. We also present benchmarking results based on a range of mod-els, which show that simple baselines are in general difficult to beat due to the skewed data distribution, but in the specific case of the respondent having submitted a response, a fine-tuned BERT model offers considerable improvements over a majority-class model
References used
https://aclanthology.org/
rate research
Read More
Strong and affordable in-domain data is a desirable asset when transferring trained semantic parsers to novel domains. As previous methods for semi-automatically constructing such data cannot handle the complexity of realistic SQL queries, we propose
to construct SQL queries via context-dependent sampling, and introduce the concept of topic. Along with our SQL query construction method, we propose a novel pipeline of semi-automatic Text-to-SQL dataset construction that covers the broad space of SQL queries. We show that the created dataset is comparable with expert annotation along multiple dimensions, and is capable of improving domain transfer performance for SOTA semantic parsers.
The present research addresses one of the new and important financial and taxation
issues, namely: the resolution of tax disputes via administrative means. It seeks out to find
resolutions for tax disputes that arise between the taxpayer and the ta
x administration when
applying the revenue law where administrative committees formed for that purpose would
undertake resolving these disputes before being referred to the judicial stage in order to
lessen the burden laid upon the judicial stage. This research discusses the importance of
identifying the nature of tax disputes, concept, characteristics, and the reasons that lead to
tax disputes between taxpayers and tax administration.
The study attempts to explain the concept of objection, its conditions and effects. The
research also addresses the administrative committees specialized in resolving tax disputes
in the administrative stage shedding the light on their formation, mechanism of work,
decisions, best formulas of taxpayer-tax administration resolutions for tax disputes, and
best solutions for the problems facing both the taxpayer and the tax administration in
resolving tax disputes arising between them via administrative means.
This paper introduces a long-range multiple-choice Question Answering (QA) dataset, based on full-length fiction book texts. The questions are formulated as 10-way multiple-choice questions, where the task is to select the correct character name give
n a character description, or vice-versa. Each character description is formulated in natural text and often contains information from several sections throughout the book. We provide 20,000 questions created from 10,000 manually annotated descriptions of characters from 177 books containing 152,917 words on average. We address the current discourse regarding dataset bias and leakage by a simple anonymization procedure, which in turn enables interesting probing possibilities. Finally, we show that suitable baseline algorithms perform very poorly on this task, with the book size itself making it non-trivial to attempt a Transformer-based QA solution. This leaves ample room for future improvement, and hints at the need for a completely different type of solution.
In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty classifier, which predicts the difficulty of sentences for language learners using either the CEFR proficiency levels or the binary classification as simple or complex. We c
ompare the use of sentence embeddings of different kinds (fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. Our best results have been achieved using fined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71 Spearman correlation for regression. Our binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.
Neural encoders of biomedical names are typically considered robust if representations can be effectively exploited for various downstream NLP tasks. To achieve this, encoders need to model domain-specific biomedical semantics while rivaling the univ
ersal applicability of pretrained self-supervised representations. Previous work on robust representations has focused on learning low-level distinctions between names of fine-grained biomedical concepts. These fine-grained concepts can also be clustered together to reflect higher-level, more general semantic distinctions, such as grouping the names nettle sting and tick-borne fever together under the description puncture wound of skin. It has not yet been empirically confirmed that training biomedical name encoders on fine-grained distinctions automatically leads to bottom-up encoding of such higher-level semantics. In this paper, we show that this bottom-up effect exists, but that it is still relatively limited. As a solution, we propose a scalable multi-task training regime for biomedical name encoders which can also learn robust representations using only higher-level semantic classes. These representations can generalise both bottom-up as well as top-down among various semantic hierarchies. Moreover, we show how they can be used out-of-the-box for improved unsupervised detection of hypernyms, while retaining robust performance on various semantic relatedness benchmarks.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
