… Thirdly, we survey the existing studies on multilingual representations and investigate
whether the … Aligning the representation of diverse languages acts as an integral part of NLP’s …

… 2022). Following that, there has been an improvement in the … study the DR challenge in
modern NLP models. To this end, we constructed a new DR benchmark comprising various NLP …

… We evaluate competitive NLP baselines on the three datasets and perform an analysis of
the predictions to better understand the limitations of such approaches. We make the datasets …

… In recent years, advances in transfer learning with deep neural networks based on … NLP
models, especially in low-resource languages. In this context, transfer learning enables training …

… sively discussed in the NLP community and made significant … In this study, we introduce a
label alignment and reassignment … 2022): This work utilizes a pluggable prompting method to …

… (MLM) objective in natural language processing tasks. They … 2022) incorporate the serialized
AST as part of the input for … in representation learning of deep learning networks. Recent …

… of various NLP applications including question answering (Yasunaga et al.… 2022), etc.
Considering facts inherently evolve in KGs over time, Temporal Knowledge Graphs (TKGs) are …

… In this paper, noting significant differences between inputs in NLP and image processing (…
can help in NLP in detecting adversarial examples using learned representations, and what …

… exceptional performance in various natural language processing (NLP) tasks. However, the …
2022… 2022) that possess extensive language knowledge and can be effectively employed in …

Large-scale pre-trained models, such as BERT, have demonstrated outstanding performance
in Natural Language Processing (NLP). Nevertheless, the high number of parameters in …