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Register a new userGeneric Oracles for Structured Prediction
الأوراج العامة للتنبؤ المنظم
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عند تعلمها دون استكشاف، تخضع النماذج المحلية لمهام التنبؤ المهيكلة لتحيز التعرض ولا يمكن تدريبها دون توجيه مفصل.التعلم التقليد النشط (AIL)، المعروف أيضا في NLP كتعلم Oracle الديناميكي، هو تقنية عامة للعمل حول هذه المشكلات من خلال السماح باستكشاف مخرجات مختلفة في وقت التدريب.تتطلب AIl ردود الفعل Oracle: Oracle هي أي خوارزمية يمكنها، بالنظر إلى حل مرشح جزئي وشروح الذهب، ابحث عن الإخراج التالي (الحد الأدنى) الإخراج التالي لإنتاج.تصف هذه الورقة تقنية حكومية محدودة عامة لاستكشاف الأوراج.تصف هذه التقنية فعالة أيضا وسوف توسيع المهام التي يمكن استخدامها بشكل كبير.
When learned without exploration, local models for structured prediction tasks are subject to exposure bias and cannot be trained without detailed guidance. Active Imitation Learning (AIL), also known in NLP as Dynamic Oracle Learning, is a general technique for working around these issues by allowing the exploration of different outputs at training time. AIL requires oracle feedback: an oracle is any algorithm which can, given a partial candidate solution and gold annotation, find the correct (minimum loss) next output to produce. This paper describes a general finite state technique for deriving oracles. The technique describe is also efficient and will greatly expand the tasks for which AIL can be used.
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Selective rationalization aims to produce decisions along with rationales (e.g., text highlights or word alignments between two sentences). Commonly, rationales are modeled as stochastic binary masks, requiring sampling-based gradient estimators, whi
ch complicates training and requires careful hyperparameter tuning. Sparse attention mechanisms are a deterministic alternative, but they lack a way to regularize the rationale extraction (e.g., to control the sparsity of a text highlight or the number of alignments). In this paper, we present a unified framework for deterministic extraction of structured explanations via constrained inference on a factor graph, forming a differentiable layer. Our approach greatly eases training and rationale regularization, generally outperforming previous work on what comes to performance and plausibility of the extracted rationales. We further provide a comparative study of stochastic and deterministic methods for rationale extraction for classification and natural language inference tasks, jointly assessing their predictive power, quality of the explanations, and model variability.
The management of electronic documents is considered as one of
the most important problems that have been discussed in this age.
That is due to its effectiveness and significance in providing a
variety of services in different governmental departm
ents. Therefore it was necessary for many countries to rely on an electronic documents management system.
Even such systems have many advantages, but they have also some cons such as the application inability in all foundations, incapability of sharing data between the foundations except through a middleware, difficulty of following up the dealings by their owner, etc. These problems are due to the absence of a generic and uniform structure between foundations. Such structure may lead to an incorporation and collaboration between these foundations. In this paper, we have made an analytical study for some systems showing their cons, then we have proposed an algorithm for following up any dealings regardless of their type or the department which will manage it. Then we proposed a design for a dynamic database which could be expanded according to the foundation's needs.
Adapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. Even with uniform word form repres
entations (MUSE, mBERT), word order discrepancies may hurt the adaptation of models. In this paper, we build structured prediction models with bag-of-words inputs, and introduce a new reordering module to organizing words following the source language order, which learns task-specific reordering strategies from a general-purpose order predictor model. Experiments on zero-shot cross-lingual dependency parsing, POS tagging, and morphological tagging show that our model can significantly improve target language performances, especially for languages that are distant from the source language.
Semantic representation that supports the choice of an appropriate connective between pairs of clauses inherently addresses discourse coherence, which is important for tasks such as narrative understanding, argumentation, and discourse parsing. We pr
opose a novel clause embedding method that applies graph learning to a data structure we refer to as a dependency-anchor graph. The dependency anchor graph incorporates two kinds of syntactic information, constituency structure, and dependency relations, to highlight the subject and verb phrase relation. This enhances coherence-related aspects of representation. We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase. We evaluate our method on two new datasets: a subset of a large corpus where the source texts are published novels, and a new dataset collected from students' essays. The results demonstrate a significant improvement over tree-based models, confirming the importance of emphasizing the subject and verb phrase. The performance gap between the two datasets illustrates the challenges of analyzing student's written text, plus a potential evaluation task for coherence modeling and an application for suggesting revisions to students.
ازدادت الحاجة لأنظمة التنبؤ المرورية وأصبحت حاجة ضرورية وملحة في أنظمة إدارة المرور المتقدمة، ذلك لأن توقع كثافة المرور يقلل الازدحام المروري ويسهل حركة السير. ومع وجود تنبؤ دقيق بحالة المرور سيكون بمقدورنا تطوير نظام إدارة مرورية متطور ونظام استعلام
ات متطور للمسافرين.
التحدي الذي يواجه مشكلة نمذجة حالة المرور هو الخصائص المعقدة للعمليات المرورية العشوائية. معلومات التسلسل الزمني للكثافة المرورية، والسرعات، والتمركز المروري والتي يتم جمعها من مواقع مختلفة تمتلك خصائص مختلفة عن بعضها، وبذلك عملية التنبؤ بالكثافة المرورية المستقبلية ليست عملية بديهية، ويناقش هذا البحث عدة طرق قامت بتقديم حلول لهذه المشكلة.
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