Recently, the emergence of pre-trained models (PTMs) has brought natural language
processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs …

Natural Language Processing (NLP) is a research field where a language in consideration
is processed to understand its syntactic, semantic, and sentimental aspects. The …

Over the last few years, large language models (LLMs) have emerged as the most important
breakthroughs in natural language processing (NLP) that fundamentally transform research …

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis
(ABSA), aiming to analyze and understand people's opinions at the aspect level, has been …

Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …

While BERT is an effective method for learning monolingual sentence embeddings for
semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019) …

Transformer-based models have pushed state of the art in many areas of NLP, but our
understanding of what is behind their success is still limited. This paper is the first survey of …

This paper demonstrates that multilingual denoising pre-training produces significant
performance gains across a wide variety of machine translation (MT) tasks. We present …

State-of-the-art unsupervised multilingual models (eg, multilingual BERT) have been shown
to generalize in a zero-shot cross-lingual setting. This generalization ability has been …

In this work, we present an information-theoretic framework that formulates cross-lingual
language model pre-training as maximizing mutual information between multilingual-multi …