Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMultilingual Image Corpus: Annotation Protocol
الصورة متعددة اللغات Corpus: بروتوكول التعليق
418
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذه الورقة، نقدم العمل في التقدم الذي يهدف إلى تطوير مجموعة بيانات جديدة للصورة مع كائنات مشروح. تتكون Corpus Image متعددة اللغات من طبولوجيا الكائنات المرئية (بناء على WordNet) ومجموعة من الصور ذات الصلة بشكل موضوعي المشروح مع أقنعة تجزئة وفئات الكائنات. حددنا 277 فصولا مهيمنة و 1،037 فصالا من الوالدين والسماء، وتجميعها إلى 10 مجالات مواضيعية مثل الرياضة والطب والتعليم والغذاء والأمن، وما إلى ذلك بالنسبة للفصول المختارة يتم إجراء بحث على شبكة الإنترنت واسعة النطاق من أجل ترجمة مجموعة كبيرة من الصور ذات حقوق الطبع والنشر عالية الجودة. إن تركيز الورق هو بروتوكول التوضيح الذي أنشأناه لتسهيل عملية التوضيحية: ontology للأشياء المرئية والاتفاقيات للاطلاع على الصورة وتجزئة الكائنات. تم تصميم DataSet لكل من تصنيف الصور والكشف عن الكائن والتجزئة الدلالي. بالإضافة إلى ذلك، سيتم تزويد التعليقات التوضيحية للكائن بأوصاف متعددة اللغات باستخدام Wordnets المتاحة بحرية.
In this paper, we present work in progress aimed at the development of a new image dataset with annotated objects. The Multilingual Image Corpus consists of an ontology of visual objects (based on WordNet) and a collection of thematically related images annotated with segmentation masks and object classes. We identified 277 dominant classes and 1,037 parent and attribute classes, and grouped them into 10 thematic domains such as sport, medicine, education, food, security, etc. For the selected classes a large-scale web image search is being conducted in order to compile a substantial collection of high-quality copyright free images. The focus of the paper is the annotation protocol which we established to facilitate the annotation process: the Ontology of visual objects and the conventions for image selection and for object segmentation. The dataset is designed both for image classification and object detection and for semantic segmentation. In addition, the object annotations will be supplied with multilingual descriptions by using freely available wordnets.
References used
https://aclanthology.org/
rate research
Read More
Multilingual pretrained language models are rapidly gaining popularity in NLP systems for non-English languages. Most of these models feature an important corpus sampling step in the process of accumulating training data in different languages, to en
sure that the signal from better resourced languages does not drown out poorly resourced ones. In this study, we train multiple multilingual recurrent language models, based on the ELMo architecture, and analyse both the effect of varying corpus size ratios on downstream performance, as well as the performance difference between monolingual models for each language, and broader multilingual language models. As part of this effort, we also make these trained models available for public use.
Emotion and empathy are examples of human qualities lacking in many human-machine interactions. The goal of our work is to generate engaging dialogue grounded in a user-shared image with increased emotion and empathy while minimizing socially inappro
priate or offensive outputs. We release the Neural Image Commenting with Empathy (NICE) dataset consisting of almost two million images and the corresponding human-generated comments, a set of human annotations, and baseline performance on a range of models. In-stead of relying on manually labeled emotions, we also use automatically generated linguistic representations as a source of weakly supervised labels. Based on these annotations, we define two different tasks for the NICE dataset. Then, we propose a novel pre-training model - Modeling Affect Generation for Image Comments (MAGIC) - which aims to generate comments for images, conditioned on linguistic representations that capture style and affect, and to help generate more empathetic, emotional, engaging and socially appropriate comments. Using this model we achieve state-of-the-art performance on one of our NICE tasks. The experiments show that the approach can generate more human-like and engaging image comments.
The dawn of the digital age led to increasing demands for digital research resources, which shall be quickly processed and handled by computers. Due to the amount of data created by this digitization process, the design of tools that enable the analy
sis and management of data and metadata has become a relevant topic. In this context, the Multilingual Corpus of Survey Questionnaires (MCSQ) contributes to the creation and distribution of data for the Social Sciences and Humanities (SSH) following FAIR (Findable, Accessible, Interoperable and Reusable) principles, and provides functionalities for end-users that are not acquainted with programming through an easy-to-use interface. By simply applying the desired filters in the graphic interface, users can build linguistic resources for the survey research and translation areas, such as translation memories, thus facilitating data access and usage.
In recent years, remote digital healthcare using online chats has gained momentum, especially in the Global South. Though prior work has studied interaction patterns in online (health) forums, such as TalkLife, Reddit and Facebook, there has been lim
ited work in understanding interactions in small, close-knit community of instant messengers. In this paper, we propose a linguistic annotation framework to facilitate analysis of health-focused WhatsApp groups. The primary aim of the framework is to understand interpersonal relationships among peer supporters in order to help develop NLP solutions for remote patient care and reduce burden of overworked healthcare providers. Our framework consists of fine-grained peer support categorization and message-level sentiment tagging. Additionally, due to the prevalence of code-mixing in such groups, we incorporate word-level language annotations. We use the proposed framework to study two WhatsApp groups in Kenya for youth living with HIV, facilitated by a healthcare provider.
Application-Level Multicast Networks are easy to deployment, it does not require
any change in the network layer, where data is sent to the network via the built-up
coverage of the tree using a single-contact transmission of the final contract, who
are the
hosts are free can join / leave whenever they want it, or even to leave without telling any
node so. Causing the separation of the children of the leaved node from the tree, and the
request for rejoin, in other words, these nodes will be separated from the overlay tree and
cannot get the data even rejoin. This causes the distortion of the constructed tree, and the
loss of several packets which can significantly impact the user.
One of the key challenges in building a multi-efficiently and effectively overlay
multicast protocol is to provide a robust mechanism to overcome the sudden departure of a
node from the overlay tree without a significant impact on the performance of the
constructed tree. In this research, we propose a new protocol to solve problems presented
previously.
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
