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Register a new userExpected Validation Performance and Estimation of a Random Variable's Maximum
أداء التحقق من الصحة المتوقع وتقدير الحد الأقصى للمتغير العشوائي
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غالبا ما يتم دعم الأبحاث في NLP من خلال النتائج التجريبية، وقد يؤدي الإبلاغ المحسن لهذه النتائج إلى فهم أفضل وأكثر استنساخا. في هذه الورقة، نحلل ثلاثة مقدرين إحصائي لأداء التحقق من الصحة المتوقع، وهي أداة تستخدم لأداء الإبلاغ (على سبيل المثال، الدقة) كدالة للميزانية الحاسوبية (على سبيل المثال، عدد تجارب ضبط HyperParameter). عندما يقوم العمل السابق بتحليل مثل هذه المقدرين يركز على التحيز، فإننا ندرس أيضا التباين ويعني الخطأ التربيعي (MSE). في كل من السيناريوهات الاصطناعية والواقعية، نقوم بتقييم ثلاثة مقدرين والعثور على مقدر غير متحيز لديه أعلى التباين، ومقدر أصغر البتان لديه أكبر تحيز؛ يقيم أصغر MSE يضرب توازن بين التحيز والتباين، وعرض مفاضلة الكلاسيكية من تباين التحيز. نحن نستخدم أداء التحقق من الصحة المتوقع للمقارنة بين النماذج المختلفة، وتحليل مدى متكرر كل مقدر يؤدي إلى رسم استنتاجات غير صحيحة حول أي نماذج تؤدي إلى الأفضل. نجد أن المقدرين المتحيزين تؤدي إلى أقل استنتاجات غير صحيحة، تلمحون بأهمية التقليل من التباين و MSE.
Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation performance, a tool used for reporting performance (e.g., accuracy) as a function of computational budget (e.g., number of hyperparameter tuning experiments). Where previous work analyzing such estimators focused on the bias, we also examine the variance and mean squared error (MSE). In both synthetic and realistic scenarios, we evaluate three estimators and find the unbiased estimator has the highest variance, and the estimator with the smallest variance has the largest bias; the estimator with the smallest MSE strikes a balance between bias and variance, displaying a classic bias-variance tradeoff. We use expected validation performance to compare between different models, and analyze how frequently each estimator leads to drawing incorrect conclusions about which of two models performs best. We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.
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We hereby present our submission to the Shared Task in Evaluating Accuracy at the INLG 2021 Conference. Our evaluation protocol relies on three main components; rules and text classifiers that pre-annotate the dataset, a human annotator that validate
s the pre-annotations, and a web interface that facilitates this validation. Our submission consists in fact of two submissions; we first analyze solely the performance of the rules and classifiers (pre-annotations), and then the human evaluation aided by the former pre-annotations using the web interface (hybrid). The code for the web interface and the classifiers is publicly available.
Wireless networks suffer from frequent loss of packets for many reasons such as
interference, collision and fading. This makes wireless medium unreliable medium for data
transfer. The main methods for ensuring the reliability in this medium are usi
ng
transmission control protocol (TCP) and the automatic repeat request (ARQ). Recently,
network coding has been found as new technology that changes the traditional forwarding
method (Store- and- Forward) in the networks to more effective and intelligent method
(Code- and- Forward), which contributes to the increase of both capacity and throughput of
these networks.
In this research, random linear network coding is used as promising technology that
aims to achieve the reliable transfer of data in losing wireless networks, and studying the
enhancement that this technology presents to the performance of these networks in unicast
and multicast transmission. For evaluating the efficiency of this technology and comparing
its performance with the performance of reliable transfer protocols, we use the networks
simulator (NS3). Simulation results showed that random linear network coding achieve the
reliable transfer of data with bigger throughput and less delay and number of transmission
compared with the protocols (TCP, ARQ).
For decades, published Automatic Signature Verification (ASV) works depended on using one feature set. Some researchers selected this feature set based on their experience, and some others selected it using some feature selection algorithms that can
select the best
feature set (bfs). In practical systems, the documents containing the signatures could be noisy, and recognition of check writer in multi-signatory accounts is required. Due to the error caused by such requirements and data quality, improving the performance of ASV
becomes a necessity. In this paper, a new technique for ASV decision making using Multi-Sets of Features is introduced. The experimental results have shown that the introduced technique gives important improvement in forgery detection and in the overall performance of the system.
Pre-trained neural language models give high performance on natural language inference (NLI) tasks. But whether they actually understand the meaning of the processed sequences is still unclear. We propose a new diagnostics test suite which allows to
assess whether a dataset constitutes a good testbed for evaluating the models' meaning understanding capabilities. We specifically apply controlled corruption transformations to widely used benchmarks (MNLI and ANLI), which involve removing entire word classes and often lead to non-sensical sentence pairs. If model accuracy on the corrupted data remains high, then the dataset is likely to contain statistical biases and artefacts that guide prediction. Inversely, a large decrease in model accuracy indicates that the original dataset provides a proper challenge to the models' reasoning capabilities. Hence, our proposed controls can serve as a crash test for developing high quality data for NLI tasks.
While annotating normalized times in food security documents, we found that the semantically compositional annotation for time normalization (SCATE) scheme required several near-duplicate annotations to get the correct semantics for expressions like
Nov. 7th to 11th 2021. To reduce this problem, we explored replacing SCATE's Sub-Interval property with a Super-Interval property, that is, making the smallest units (e.g., 7th and 11th) rather than the largest units (e.g., 2021) the heads of the intersection chains. To ensure that the semantics of annotated time intervals remained unaltered despite our changes to the syntax of the annotation scheme, we applied several different techniques to validate our changes. These validation techniques detected and allowed us to resolve several important bugs in our automated translation from Sub-Interval to Super-Interval syntax.
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