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Register a new userContrapositive Local Class Inference
استنتاج الطبقة المحلية مواضيع
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قد يتم تنفيذ أنواع معينة من مشاكل التصنيف على مستويات متعددة من الحبيبات؛ على سبيل المثال، قد نريد معرفة قطبية المعنويات وثيقة أو جملة أو عبارة. في كثير من الأحيان، قد يكون التنبؤ في سياق أكبر (على سبيل المثال، الجمل أو الفقرات) أمرا مفيدا للتنبؤ أكثر تجميعية في وحدة دلالية أصغر (مثل الكلمات أو العبارات). ومع ذلك، قد يستنتج بشكل مباشر أكثر الميزات المحلية البارزة من التنبؤ العالمي من التوقعات العالمية في دلالات هذه العلاقة. يجادل هذا العمل بأن الاستدلال على طول العلاقة بين المواجهة بالتنبؤ المحلي والتنبؤ العالمي المقابل يجعل إطار الاستدلال أكثر دقة وقوية للضوضاء. نوضح كيف يمكن تنفيذ إطار الخازن هذا كدالة نقل تعمل على إعادة كتابة سياق أكبر من فئة واحدة إلى أخرى وإظهار كيفية تدريب وظيفة النقل المناسبة من كوربوس الناتج عن المستخدم صاخبة. تتحقق النتائج التجريبية البصيرة الخاصة بنا أن إطار المواقد المقترح يتفوق على النهج البديلة على مجالات المشكلات المقيدة بالموارد.
Certain types of classification problems may be performed at multiple levels of granularity; for example, we might want to know the sentiment polarity of a document or a sentence, or a phrase. Often, the prediction at a greater-context (e.g., sentences or paragraphs) may be informative for a more localized prediction at a smaller semantic unit (e.g., words or phrases). However, directly inferring the most salient local features from the global prediction may overlook the semantics of this relationship. This work argues that inference along the contraposition relationship of the local prediction and the corresponding global prediction makes an inference framework that is more accurate and robust to noise. We show how this contraposition framework can be implemented as a transfer function that rewrites a greater-context from one class to another and demonstrate how an appropriate transfer function can be trained from a noisy user-generated corpus. The experimental results validate our insight that the proposed contrapositive framework outperforms the alternative approaches on resource-constrained problem domains.
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https://aclanthology.org/
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Achieving the intended purpose of the local administration requires carful selection of the
elements assigned to work in local units, which requires that those elements are aware of
local reality and have sufficient technical expertise. In the cont
ext of the inability of the
method used in forming local councils to achieve the objectives of local administration, the
jurisprudence and legislation differ in determining the method to be followed within the
priority of those objectives and within the political ،economic ،and social reality in each country.
Where some systems tend to adopt the election in selecting the members of local councils ,
while others tend to adopt the method of appointing or the combination of (election and
appointing) methods in order to take the advantages of both methods . But, it is the need
and desire of prevailing legal system and the possibilities available , that determine the
method to be adopted.
Early exit mechanism aims to accelerate the inference speed of large-scale pre-trained language models. The essential idea is to exit early without passing through all the inference layers at the inference stage. To make accurate predictions for down
stream tasks, the hierarchical linguistic information embedded in all layers should be jointly considered. However, much of the research up to now has been limited to use local representations of the exit layer. Such treatment inevitably loses information of the unused past layers as well as the high-level features embedded in future layers, leading to sub-optimal performance. To address this issue, we propose a novel Past-Future method to make comprehensive predictions from a global perspective. We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions. Extensive experiments demonstrate that our method outperforms previous early exit methods by a large margin, yielding better and robust performance.
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