Does Vision-and-Language Pretraining Improve Lexical Grounding?

Linguistic representations derived from text alone have been criticized for their lack of grounding, i.e., connecting words to their meanings in the physical world. Vision-and- Language (VL) models, trained jointly on text and image or video data, have been offered as a response to such criticisms. However, while VL pretraining has shown success on multimodal tasks such as visual question answering, it is not yet known how the internal linguistic representations themselves compare to their text-only counterparts. This paper compares the semantic representations learned via VL vs. text-only pretraining for two recent VL models using a suite of analyses (clustering, probing, and performance on a commonsense question answering task) in a language-only setting. We find that the multimodal models fail to significantly outperform the text-only variants, suggesting that future work is required if multimodal pretraining is to be pursued as a means of improving NLP in general.

mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model

Domain adaptive pretraining, i.e. the continued unsupervised pretraining of a language model on domain-specific text, improves the modelling of text for downstream tasks within the domain. Numerous real-world applications are based on domain-specific text, e.g. working with financial or biomedical documents, and these applications often need to support multiple languages. However, large-scale domain-specific multilingual pretraining data for such scenarios can be difficult to obtain, due to regulations, legislation, or simply a lack of language- and domain-specific text. One solution is to train a single multilingual model, taking advantage of the data available in as many languages as possible. In this work, we explore the benefits of domain adaptive pretraining with a focus on adapting to multiple languages within a specific domain. We propose different techniques to compose pretraining corpora that enable a language model to both become domain-specific and multilingual. Evaluation on nine domain-specific datasets---for biomedical named entity recognition and financial sentence classification---covering seven different languages show that a single multilingual domain-specific model can outperform the general multilingual model, and performs close to its monolingual counterpart. This finding holds across two different pretraining methods, adapter-based pretraining and full model pretraining.

Inadequacy of the Syrian Juvenile law to computers Juvenile

It is clear that the jurisdiction of juvenile court is stated in any crime committed by the juvenile whether it is a traditional crime or a new crime such as informatics crimes. Thus, a question arises about the importance of the Syrian juvenile Law No. 18 of 1974 which was amended by Law 51 of 1979, to be applied on the informatics juvenile delinquency in the absence of specialized judicial bodies concerned with juvenile cases.

القضاء الإداري الأردني و المحاكمة العادلة

إن تمتع الإنسان بمحاكمة عادلة هو من الحقوق الأساسية التي تكفلها الدساتير و تنظمها التشريعات، سواء من حيث المساواة بينهم أمام مرفق القضاء، أو من حيث تمكين الأفراد من الدفاع عن أنفسهم بشكل كامل، أو حقهم بمتابعة النزاع و الطعن و الاعتراض بأحكام القضاء لدى المراجع القضائية العليا. يمثل قانون محكمة العدل العليا رقم 12 لسنة 1992 الأساس التشريعي لتنظيم القضاء الإداري في الأردن.