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Register a new userContemporary NLP Modeling in Six Comprehensive Programming Assignments
النمذجة NLP المعاصرة في ستة مهام برمجة شاملة
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نقدم سلسلة من مهام البرمجة، قابلة للتكيف مع مجموعة من مستويات الخبرة من المرحلة الجامعية المتقدمة إلى الدكتوراه، لتعليم الطلاب تصميم وتنفيذ أنظمة NLP الحديثة. يتم بناء هذه المهام من الألف إلى الياء والتأكيد على فهم المكدس الكامل للنماذج التعليمية الآلية: في البداية، يقوم الطلاب بتنفيذ حساب الاستدلال والتدرج باليد، ثم استخدم Pytorch لبناء شبكات عصبية أحدث تقريبا باستخدام أفضل الممارسات الحالية. يتم اختيار الموضوعات لتغطية مجموعة واسعة من تقنيات النمذجة والاستدلال التي قد تواجه المرء، بدءا من النماذج الخطية المناسبة لتطبيقات الصناعة لنماذج التعلم العميق الحديثة المستخدمة في أبحاث NLP. يتم تخصيص المهام، مع خيارات مقيدة لتوجيه طلاب أقل خبرة أو الخيارات المفتوحة المنتهية لإعطاء حرية الطلاب المتقدمة لاستكشافها. يمكن نشر كل منهم بطريقة غير قابلة للتطبيق بالكامل، وقد تم اختبارها بشكل جماعي على أكثر من 300 طالب عبر العديد من الفصول الدراسية.
We present a series of programming assignments, adaptable to a range of experience levels from advanced undergraduate to PhD, to teach students design and implementation of modern NLP systems. These assignments build from the ground up and emphasize full-stack understanding of machine learning models: initially, students implement inference and gradient computation by hand, then use PyTorch to build nearly state-of-the-art neural networks using current best practices. Topics are chosen to cover a wide range of modeling and inference techniques that one might encounter, ranging from linear models suitable for industry applications to state-of-the-art deep learning models used in NLP research. The assignments are customizable, with constrained options to guide less experienced students or open-ended options giving advanced students freedom to explore. All of them can be deployed in a fully autogradable fashion, and have collectively been tested on over 300 students across several semesters.
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We present a set of assignments for a graduate-level NLP course. Assignments are designed to be interactive, easily gradable, and to give students hands-on experience with several key types of structure (sequences, tags, parse trees, and logical form
s), modern neural architectures (LSTMs and Transformers), inference algorithms (dynamic programs and approximate search) and training methods (full and weak supervision). We designed assignments to build incrementally both within each assignment and across assignments, with the goal of enabling students to undertake graduate-level research in NLP by the end of the course.
We describe two Jupyter notebooks that form the basis of two assignments in an introductory Natural Language Processing (NLP) module taught to final year undergraduate students at Dublin City University. The notebooks show the students how to train a
bag-of-words polarity classifier using multinomial Naive Bayes, and how to fine-tune a polarity classifier using BERT. The students take the code as a starting point for their own experiments.
Broader disclosive transparency---truth and clarity in communication regarding the function of AI systems---is widely considered desirable. Unfortunately, it is a nebulous concept, difficult to both define and quantify. This is problematic, as previo
us work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where too much information'' clouds a reader's understanding of what a system description means. Disclosive transparency's subjective nature has rendered deep study into these problems and their remedies difficult. To improve this state of affairs, We introduce neural language model-based probabilistic metrics to directly model disclosive transparency, and demonstrate that they correlate with user and expert opinions of system transparency, making them a valid objective proxy. Finally, we demonstrate the use of these metrics in a pilot study quantifying the relationships between transparency, confusion, and user perceptions in a corpus of real NLP system descriptions.
Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models. Attention redundancy has been observed among attention heads but has not been deeply studied in the literature. Using BERT-base model as an example, t
his paper provides a comprehensive study on attention redundancy which is helpful for model interpretation and model compression. We analyze the attention redundancy with Five-Ws and How. (What) We define and focus the study on redundancy matrices generated from pre-trained and fine-tuned BERT-base model for GLUE datasets. (How) We use both token-based and sentence-based distance functions to measure the redundancy. (Where) Clear and similar redundancy patterns (cluster structure) are observed among attention heads. (When) Redundancy patterns are similar in both pre-training and fine-tuning phases. (Who) We discover that redundancy patterns are task-agnostic. Similar redundancy patterns even exist for randomly generated token sequences. (Why'') We also evaluate influences of the pre-training dropout ratios on attention redundancy. Based on the phase-independent and task-agnostic attention redundancy patterns, we propose a simple zero-shot pruning method as a case study. Experiments on fine-tuning GLUE tasks verify its effectiveness. The comprehensive analyses on attention redundancy make model understanding and zero-shot model pruning promising.
Syrian narrative writers have used a number of symbols in
their narrative works, one of which is fire, which has been used very
flexibly. Fire stood for punishment, catharsis, fear, and desire both in
relation to the theme of the relevant story an
d its title.
The paper leaves it open for new significations for this symbol
that might appear in new meanings and forms by other narrative
writers not dealt with here.
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