Pre-trained Models for Natural Language Processing: A Survey (2003.08271v4)

Abstract: Recently, the emergence of pre-trained models (PTMs) has brought 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 with four perspectives. Next, we describe how to adapt the knowledge of PTMs to the 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.

Pre-trained Models for Natural Language Processing: An Expert Analysis

The paper "Pre-trained Models for Natural Language Processing: A Survey" by Xipeng Qiu et al. presents an extensive review of pre-trained models (PTMs) in NLP. This comprehensive survey explores the developmental timeline, taxonomy, challenges, and promising directions of PTMs, positioning itself as an important reference guide for experienced researchers in the field.

Background on Language Representation Learning

The paper begins with a discussion on the fundamental principles of language representation learning, emphasizing the transition from handcrafted features in traditional NLP to distributed representations learned by neural networks. The advantage of neural methods lies in their ability to implicitly capture syntactic, semantic, and even pragmatic features through dense vectors, thus reducing the burden of feature engineering.

Evolution of Pre-trained Models

First-Generation PTMs

The first-generation PTMs, exemplified by word embeddings such as Skip-Gram and GloVe, focused on learning static word representations from text data. Despite capturing semantic meanings, these models are context-free and fail to grasp higher-level concepts like syntactic structures or word sense disambiguation.

Second-Generation PTMs

The second-generation PTMs shifted focus towards contextual word embeddings. Notable models in this category include CoVe, ELMo, OpenAI GPT, and BERT. These models leverage powerful architectures such as LSTMs and Transformers to encapsulate word meanings in context, achieving significant improvements across various NLP tasks.

Taxonomy of PTMs

One of the key contributions of the paper is the proposed taxonomy of PTMs, which categorizes existing models from four perspectives:

1. Representation Type: Distinguishing between non-contextual (e.g., Word2Vec) and contextual embeddings (e.g., BERT).
2. Architecture: Including LSTM-based, Transformer encoder-based, and full Transformer architectures.
3. Pre-Training Tasks: Encompassing supervised learning, unsupervised learning, and self-supervised learning, each tailored for different aspects of language understanding.
4. Extensions: Addressing variations such as knowledge-enriched, multilingual, and domain-specific PTMs.

Pre-Training Tasks

The authors go in-depth into various pre-training tasks that undergird PTMs:

• LLMing (LM): Both unidirectional and bidirectional models capture rich contextual information.
• Masked LLMing (MLM): Advanced by models like BERT, this task mitigates the limitations of unidirectional LMs by predicting masked tokens within an input sequence.
• Permuted LLMing (PLM): Introduced by XLNet, aiming to address the prediction alignment issue by considering permutations of input sequences.
• Denoising Autoencoder (DAE): Applied in models like BART to reconstruct original text from corrupted inputs, enriching the robustness of representations.
• Contrastive Learning (CTL): Involving tasks like Deep InfoMax and Replaced Token Detection, focusing on distinguishing real from fake samples to enhance model understanding.

Implications and Future Directions

Practical Implications

The practical implications of the research are broad, impacting tasks from text classification and sentiment analysis to machine translation and summarization. For example, PTMs like BERT have set new benchmarks on tasks within the GLUE and SuperGLUE benchmarks, highlighting the transformative influence of PTMs on model performance.

Theoretical Implications

The theoretical contributions emphasize the importance of model architecture and pre-training tasks in capturing diverse linguistic phenomena. The taxonomy and comprehensive review aid in understanding how different training paradigms contribute to language understanding.

Future Developments

The survey envisions several future developments in PTMs:

1. Scaling and Efficiency: Exploring more efficient training techniques and model architectures to handle larger datasets and longer input sequences.
2. Specialized PTMs: Developing task-specific and domain-specific PTMs that leverage existing general models through methods like model compression or additional fine-tuning.
3. Interpretability and Robustness: Improving the interpretability of PTMs to understand their decision-making process and enhancing robustness against adversarial attacks.

Conclusion

This survey by Qiu et al. provides a rigorous and insightful overview of pre-trained models in NLP. Its detailed analysis of the evolution, taxonomy, and future directions of PTMs serves as a valuable guide for researchers aiming to further advance the capabilities and applications of PTMs in natural language processing.