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Pre-trained Models for Natural Language Processing: A Survey

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TLDR
Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era as mentioned in this paper, and a comprehensive review of PTMs for NLP can be found in this survey.
Abstract
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 for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy from four different perspectives. Next, we describe how to adapt the knowledge of PTMs to downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

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Self-supervised Learning: Generative or Contrastive.

TL;DR: This survey takes a look into new self-supervised learning methods for representation in computer vision, natural language processing, and graph learning, and comprehensively review the existing empirical methods into three main categories according to their objectives.
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Deep Learning--based Text Classification: A Comprehensive Review

TL;DR: This paper provided a comprehensive review of more than 150 deep learning-based models for text classification developed in recent years, and discussed their technical contributions, similarities, and strengths, and provided a quantitative analysis of the performance of different deep learning models on popular benchmarks.
Journal ArticleDOI

Pre-trained Models for Natural Language Processing: A Survey

TL;DR: This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.
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Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users

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Journal ArticleDOI

A Survey of Large Language Models

TL;DR: Recently, a large language model (LLM) as mentioned in this paper has been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks.
References
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Proceedings ArticleDOI

Glove: Global Vectors for Word Representation

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

TL;DR: A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Proceedings Article

Distributed Representations of Words and Phrases and their Compositionality

TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Proceedings Article

Neural Machine Translation by Jointly Learning to Align and Translate

TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
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