We present BERTweet, the first public large-scale pre-trained language model for English
Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is …

Pretrained contextualized embeddings are powerful word representations for structured
prediction tasks. Recent work found that better word representations can be obtained by …

Both performance and efficiency are crucial factors for sequence labeling tasks in many real-
world scenarios. Although the pre-trained models (PTMs) have significantly improved the …

Many models that leverage knowledge graphs (KGs) have recently demonstrated
remarkable success in question answering (QA) tasks. In the real world, many facts …

Transfer Learning has been shown to be a powerful tool for Natural Language Processing
(NLP) and has outperformed the standard supervised learning paradigm, as it takes benefit …

The Transformer-based model have made significant strides in semantic matching tasks by
capturing connections between phrase pairs. However, to assess the relevance of sentence …

There is a huge performance gap between formal and informal language understanding
tasks. The recent pre-trained models that improved the performance of formal language …

Ubiquitous communication on social media has led to a rapid increase in the proliferation of
unreliable information. Its ill-effects have perhaps been seen most obviously during the …

This first-of-its-kind paper presents a novel approach named PASAD that detects changes in
perceptually fluent speech acoustics of young children. Particularly, analysis of perceptually …

Natural language-based financial forecasting is an active area of research, with the majority
of publications centering around company stock prediction. There is, however, a huge …