Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userExtraction of Common-Sense Relations from Procedural Task Instructions using BERT
استخراج علاقات المعنى المشترك من تعليمات المهام الإجرائية باستخدام بيرت
209
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
المعرفة المعنية بالمعالجة المعنية ذات الصلة أمر بالغ الأهمية لدعم تخطيط الإجراءات للمهام المعقدة. على وجه الخصوص، يمكن استخدام معلومات الأدوات بما يمكن القيام به مع أدوات معينة للحد من مساحة البحث التي تنمو بشكل كبير مع عدد خيارات قابلة للحياة. توفر مصادر نموذجية لمثل هذه المعرفة، وقواعد المعرفة المعيشية المعيشية منظم مثل Cysisnet أو Child Webchild، كمية محدودة من المعلومات التي تختلف أيضا بشكل كبير عبر المجالات المختلفة. بالنظر إلى النجاح الأخير لنماذج اللغة المدربة مسبقا مثل بيرت، فإننا نبحث في ما إذا كان يمكن استخراج معلومات الإحالة المشتركة مباشرة من النص شبه المنظم بذل جهد شرح مقبول. بشكل ملموس، قارنا علاقات المعيشية المشتركة التي تم الحصول عليها من المفاهيم مقابل تلك المستخرجة مع Bert من قواعد بيانات وصفة كبيرة. في هذا السياق، نقترح وظيفة التهديف، بناء على تصنيف Wordnet TEXT لمطابقة شروط محددة لأكثر عمومية، مما يتيح تقييم غني مقابل مجموعة من علاقات الحقيقة الأرضية.
Manipulation-relevant common-sense knowledge is crucial to support action-planning for complex tasks. In particular, instrumentality information of what can be done with certain tools can be used to limit the search space which is growing exponentially with the number of viable options. Typical sources for such knowledge, structured common-sense knowledge bases such as ConceptNet or WebChild, provide a limited amount of information which also varies drastically across different domains. Considering the recent success of pre-trained language models such as BERT, we investigate whether common-sense information can directly be extracted from semi-structured text with an acceptable annotation effort. Concretely, we compare the common-sense relations obtained from ConceptNet versus those extracted with BERT from large recipe databases. In this context, we propose a scoring function, based on the WordNet taxonomy to match specific terms to more general ones, enabling a rich evaluation against a set of ground-truth relations.
References used
https://aclanthology.org/
rate research
Read More
This paper presents an automatic method to evaluate the naturalness of natural language generation in dialogue systems. While this task was previously rendered through expensive and time-consuming human labor, we present this novel task of automatic
naturalness evaluation of generated language. By fine-tuning the BERT model, our proposed naturalness evaluation method shows robust results and outperforms the baselines: support vector machines, bi-directional LSTMs, and BLEURT. In addition, the training speed and evaluation performance of naturalness model are improved by transfer learning from quality and informativeness linguistic knowledge.
Natural language processing (NLP) tasks, ranging from text classification to text generation, have been revolutionised by the pretrained language models, such as BERT. This allows corporations to easily build powerful APIs by encapsulating fine-tuned
BERT models for downstream tasks. However, when a fine-tuned BERT model is deployed as a service, it may suffer from different attacks launched by the malicious users. In this work, we first present how an adversary can steal a BERT-based API service (the victim/target model) on multiple benchmark datasets with limited prior knowledge and queries. We further show that the extracted model can lead to highly transferable adversarial attacks against the victim model. Our studies indicate that the potential vulnerabilities of BERT-based API services still hold, even when there is an architectural mismatch between the victim model and the attack model. Finally, we investigate two defence strategies to protect the victim model, and find that unless the performance of the victim model is sacrificed, both model extraction and adversarial transferability can effectively compromise the target models.
Geographic Information Systems (GIS) present one of the most important 3D modeling techniques of cities, which has become a very important and necessary for representing of contemporary cities, and doing different analyses, with the aim of finding so
lutions to the problems raised through a virtual model of reality. In addition, 3D GIS play an important role in urban planning. Despite the presence of a large number of programs capable of dealing with 3D objects, many of these applications require high technologies and advanced tools for the representation and analysis of 3D objects. The objects of 3D models represented in GIS can be associated with a set of queryable attributes, and this is not available in other 3D modeling software. This Research aims to highlight the importance of procedural 3D modeling of cities in GIS environment using ESRI CityEngine. A set of CGA (computer generated architecture) rules - programmed by procedural modeling language in CityEngine- will be presented to generate realistic 3D models that can represent all elements of cities and its infrastructure. Then, all the objects will be exported to a Web Scene viewer, which allows the creation a comprehensive platform to present a system to support decision-making, and facilitate project management related to the management of cities. The research methods will be applied to Tartous governorate through designing and modeling of proposed residential area. This study will include CGA rules applied to create a 3D model of proposed area, it will show the importance of GIS in modeling and representation of cities.
Generally, the environmental constitutional regulations used by countries vary
between objective ones made to legalize the right for a safe
environment and procedural ones. This paper focuses on and
analyzes the methods that must be used when making decisions
related to environmental protection.
organizations
Information
الدستور
الرقابة القضائية
Constitution
الجمعيات
المعلومات البيئية
المشاركة العامة
القرارات البيئية
المصلحة في الدعوى
التشريعات الخاصة بالبيئة
الهيئات و الجهات المعنية بالبيئة
public cooperation
environmental decisions
judicial control
interest in cases
Environmental legislation
Environmental Authorities and Authorities
المزيد..
Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion. Moreover, when combined with regular learning from examples, this idea yields impressive few-shot
results for a wide range of text classification tasks. It is also a promising direction to improve data efficiency in generative settings, but there are several challenges to using a combination of task descriptions and example-based learning for text generation. In particular, it is crucial to find task descriptions that are easy to understand for the pretrained model and to ensure that it actually makes good use of them; furthermore, effective measures against overfitting have to be implemented. In this paper, we show how these challenges can be tackled: We introduce GenPET, a method for text generation that is based on pattern-exploiting training, a recent approach for combining textual instructions with supervised learning that only works for classification tasks. On several summarization and headline generation datasets, GenPET gives consistent improvements over strong baselines in few-shot settings.
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
