Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMicromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health
ميكرومودال لأنظمة فعالة وتفسير وقابلة لإعادة الاستخدام: دراسة حالة حول الصحة العقلية
375
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
العديد من النماذج الإحصائية لها دقة عالية على معايير الاختبار، ولكنها ليست تفسيرها، لا يمكن إعادة استخدام النضال في سيناريوهات الموارد المنخفضة، ولا يمكن إعادة استخدامها لمهام متعددة، ولا يمكن دمج خبرات المجال بسهولة.هذه العوامل تحد من استخدامها، لا سيما في إعدادات الصحة العقلية، حيث من الصعب التعليق على مجموعات البيانات والنواتج النموذجية لها تأثير كبير.نقدم بنية micromodel لمعالجة هذه التحديات.يسمح نهجنا الباحثين ببناء تمثيلات قابلة للتفسير تضمين معرفة المجال وتقديم توضيحات خلال عملية قرار النموذج.نوضح الفكرة على مهام الصحة العقلية المتعددة: تصنيف الاكتئاب، تصنيف PTSD، وتقييم المخاطر الانتحارية.تنتج أنظمتنا باستمرار نتائج قوية، حتى في سيناريوهات الموارد المنخفضة، وهي أكثر تفسيرا من الأساليب البديلة.
Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise. These factors limit their use, particularly in settings such as mental health, where it is difficult to annotate datasets and model outputs have significant impact. We introduce a micromodel architecture to address these challenges. Our approach allows researchers to build interpretable representations that embed domain knowledge and provide explanations throughout the model's decision process. We demonstrate the idea on multiple mental health tasks: depression classification, PTSD classification, and suicidal risk assessment. Our systems consistently produce strong results, even in low-resource scenarios, and are more interpretable than alternative methods.
References used
https://aclanthology.org/
rate research
Read More
Developing documentation guidelines and easy-to-use templates for datasets and models is a challenging task, especially given the variety of backgrounds, skills, and incentives of the people involved in the building of natural language processing (NL
P) tools. Nevertheless, the adoption of standard documentation practices across the field of NLP promotes more accessible and detailed descriptions of NLP datasets and models, while supporting researchers and developers in reflecting on their work. To help with the standardization of documentation, we present two case studies of efforts that aim to develop reusable documentation templates -- the HuggingFace data card, a general purpose card for datasets in NLP, and the GEM benchmark data and model cards with a focus on natural language generation. We describe our process for developing these templates, including the identification of relevant stakeholder groups, the definition of a set of guiding principles, the use of existing templates as our foundation, and iterative revisions based on feedback.
With mental health as a problem domain in NLP, the bulk of contemporary literature revolves around building better mental illness prediction models. The research focusing on the identification of discussion clusters in online mental health communitie
s has been relatively limited. Moreover, as the underlying methodologies used in these studies mainly conform to the traditional machine learning models and statistical methods, the scope for introducing contextualized word representations for topic and theme extraction from online mental health communities remains open. Thus, in this research, we propose topic-infused deep contextualized representations, a novel data representation technique that uses autoencoders to combine deep contextual embeddings with topical information, generating robust representations for text clustering. Investigating the Reddit discourse on Post-Traumatic Stress Disorder (PTSD) and Complex Post-Traumatic Stress Disorder (C-PTSD), we elicit the thematic clusters representing the latent topics and themes discussed in the r/ptsd and r/CPTSD subreddits. Furthermore, we also present a qualitative analysis and characterization of each cluster, unraveling the prevalent discourse themes.
This Research aims to:
· Shed light on various aspects of administrative process re-engineering method
in terms of concept, implementation mechanism and its benefits.
· The need of the Palestinian universities as academy institutions to apply the
reengineering
method.
· Give a clear picture for applying re-engineering method which used to
improve the provided service to beneficiaries, increase its efficiency and to
achieve Total QualityManagement “TQM”.
This paper aims at providing a comprehensive overview of recent developments in dialogue state tracking (DST) for task-oriented conversational systems. We introduce the task, the main datasets that have been exploited as well as their evaluation metr
ics, and we analyze several proposed approaches. We distinguish between static ontology DST models, which predict a fixed set of dialogue states, and dynamic ontology models, which can predict dialogue states even when the ontology changes. We also discuss the model's ability to track either single or multiple domains and to scale to new domains, both in terms of knowledge transfer and zero-shot learning. We cover a period from 2013 to 2020, showing a significant increase of multiple domain methods, most of them utilizing pre-trained language models.
Recent work has adopted models of pragmatic reasoning for the generation of informative language in, e.g., image captioning. We propose a simple but highly effective relaxation of fully rational decoding, based on an existing incremental and characte
r-level approach to pragmatically informative neural image captioning. We implement a mixed, fast' and slow', speaker that applies pragmatic reasoning occasionally (only word-initially), while unrolling the language model. In our evaluation, we find that increased informativeness through pragmatic decoding generally lowers quality and, somewhat counter-intuitively, increases repetitiveness in captions. Our mixed speaker, however, achieves a good balance between quality and informativeness.
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
