Transformer-based pretrained language models (T-PTLMs) have achieved great success in
almost every NLP task. The evolution of these models started with GPT and BERT. These …

Transformer-based pretrained language models (PLMs) have started a new era in modern
natural language processing (NLP). These models combine the power of transformers …

Most widely used pre-trained language models operate on sequences of tokens
corresponding to word or subword units. By comparison, token-free models that operate …

Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet
nearly all commonly used models still require an explicit tokenization step. While recent …

State-of-the-art models in natural language processing rely on separate rigid subword
tokenization algorithms, which limit their generalization ability and adaptation to new …

The social networks and the rapid development of new technologies have led to
considerable changes in the tourism industry. Artificial intelligence, in particular natural …

Objective Accurate extraction of breast cancer patients' phenotypes is important for clinical
decision support and clinical research. This study developed and evaluated cancer domain …

What are the units of text that we want to model? From bytes to multi-word expressions, text
can be analyzed and generated at many granularities. Until recently, most natural language …

Transfer learning is a technique utilized in deep learning applications to transmit learned
inference to a different target domain. The approach is mainly to solve the problem of a few …

This paper describes the development of a multilingual, manually annotated dataset for
three under-resourced Dravidian languages generated from social media comments. The …