Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

Transformer architectures are highly expressive because they use self-attention
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …

We present EASE, a novel method for learning sentence embeddings via contrastive
learning between sentences and their related entities. The advantage of using entity …

The application of data analytics in management has become a crucial success factor for the
modern enterprise. To apply analytical models, appropriately prepared data must be …

Adopting Large Language Models (LLMs) creates opportunities for organizations to
increase efficiency, particularly in sentiment analysis and information extraction tasks. This …

Literature reviews constitute an indispensable component of research endeavors; however,
they often prove laborious and time-intensive. This study explores the potential of ChatGPT …

Commonsense reasoning (CSR) requires the model to be equipped with general world
knowledge. While CSR is a language-agnostic process, most comprehensive knowledge …

Abstract Language model pre-training and the derived general-purpose methods have
reshaped machine learning research. However, there remains considerable uncertainty …

There are two primary approaches to addressing cross-lingual transfer: multilingual pre-
training, which implicitly aligns the hidden representations of various languages, and …

Knowledge-enhanced language representation learning has shown promising results
across various knowledge-intensive NLP tasks. However, prior methods are limited in …