تسأل هذه الورقة عما إذا كانت استقراء توزيع الفضاء المخفي لأمثلة النصية من فئة واحدة إلى أخرى هي تحيز حثي صالح لتعزيز البيانات.لتفعيل هذا السؤال، أقترح بروتوكول تكبير بيانات بسيط يسمى استقراء مثال جيد بما فيه الكفاية "(GE3).GE3 خفيف الوزن وليس له فرطيات.تطبق على ثلاث مجموعات بيانات تصنيف النص لمختلف سيناريوهات عدم توازن البيانات، تعمل GE3 على تحسين الأداء أكثر من عمليات التصميم وغيرها من طرق تكبير بيانات الفضاء المخفية.
This paper asks whether extrapolating the hidden space distribution of text examples from one class onto another is a valid inductive bias for data augmentation. To operationalize this question, I propose a simple data augmentation protocol called good-enough example extrapolation'' (GE3). GE3 is lightweight and has no hyperparameters. Applied to three text classification datasets for various data imbalance scenarios, GE3 improves performance more than upsampling and other hidden-space data augmentation methods.
References used
https://aclanthology.org/
This paper identifies some common and specific pitfalls in the development of sign language technologies targeted at deaf communities, with a specific focus on signing avatars. It makes the call to urgently interrogate some of the ideologies behind t
Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis. Therefore, prediction accuracy is an important criteria which also necessitates robust predictive language models. However, the models using
Sub-tasks of intent classification, such as robustness to distribution shift, adaptation to specific user groups and personalization, out-of-domain detection, require extensive and flexible datasets for experiments and evaluation. As collecting such
This paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team. We used and compared a variety of preprocessing techniques, vectorization methods,
This paper presents a method integrating database with Jgroup
based on Hibernate, which is one of Object Relational Mapping
tools. We compare between the performance of Jgroup integrated
with Hibernate and the performance of RMI integrated with
Hibernate. The results show that Jgroup/Hibernate outperforms
RMI/Hibernate when the number of clients increases.