BERT and Pre-trained Language Models: Evolution and Advancements
Table of Contents:
- Introduction
- History and Background
2.1 Word Embeddings in NLP
2.2 Lookup Tables and Pre-trained Representations
2.3 Word2Vec and GloVe
2.4 The Limitations of Context-Free Word Embeddings
- The Need for Contextual Word Representations
3.1 Word Sense Embeddings
3.2 The Importance of Contextual Representations
- Contextual Representations: From ELMo to BERT
4.1 Semi-supervised Sequence Learning
4.2 The Emergence of ELMo
4.3 Introduction to BERT
- Understanding BERT: History and Advancements
5.1 BERT: The Bridge between Neural Networks and NLP
5.2 The Importance of Transformer in BERT
5.3 XLNet: Exploring the Limits of Transfer Learning
5.4 ALBERT: Lite BERT for Self-Supervised Learning
5.5 T5: Pushing the Boundaries of Transfer Learning
5.6 ELECTRA: A Novel Approach to Pre-training
- Model Distillation: Making BERT Serveable
- Conclusion: The Success and Challenges of Pre-trained Models
Article:
Introduction
In the field of Natural Language Processing (NLP), pre-trained models have become the cornerstone of advanced language processing tasks. One such model, BERT (Bidirectional Encoder Representations from Transformers), has gained immense popularity in recent years due to its remarkable performance in a wide range of NLP tasks. However, BERT is not the first model of its kind. As we Delve into the history and background of pre-trained models in NLP, we'll explore the origins of word embeddings, the limitations of Context-free representations, and the need for contextual word representations.
History and Background
Word Embeddings in NLP
Word embeddings, which are widely used in NLP, form the basis for neural networks' effectiveness in processing natural language. These embeddings provide a way to bridge the gap between continuous space, where neural networks operate, and the discrete space of text. Initially, word embeddings were learned discriminatively, with a Lookup table mapping each word in a vocabulary to a learned vector. While these embeddings were instrumental in training language models, they lacked contextual understanding.
The Limitations of Context-Free Word Embeddings
The biggest drawback of context-free word embeddings is that they treat each occurrence of a word as having the same meaning, regardless of its context. For example, the word "bank" would have the same embedding whether it refers to a financial institution or a riverbank. Various techniques, such as word Sense embeddings, have attempted to capture different senses of words, but the problem remains complex due to the vast array of meanings words can have Based on context.
The Need for Contextual Word Representations
To truly understand language, we require word representations that account for their contextual usage within a sentence. Contextual representations consider the complete sentence a word appears in and provide a more accurate portrayal of its meaning. This is a crucial advancement in NLP, as it allows for more precise semantic and syntactic analysis of text data.
Word Sense Embeddings
Word sense embeddings aim to capture the various senses of a word within its entire sentence. For example, distinguishing between different uses of the word "bank" in sentences like "I went to the bank to open an account" and "I sat by the river bank" is crucial. Despite efforts to Create word sense embeddings, accurately representing the multiple senses of words remains a challenge.
The Importance of Contextual Representations
Contextual representations, as introduced by ELMo (Embeddings from Language Models), provide a solution to the limitations of context-free embeddings. ELMo was the first well-known model to leverage contextual representations effectively. By pretraining a language model and fine-tuning it for specific tasks, ELMo significantly improved performance in various NLP challenges like question-answering and syntactic parsing.
Understanding BERT: History and Advancements
BERT stands out as a groundbreaking model that took contextual representations to new heights. Building upon previous work, BERT incorporated advancements like Transformer architectures, which revolutionized NLP models. This section explores the evolution of models leading up to BERT and highlights subsequent innovations.
BERT: The Bridge between Neural Networks and NLP
BERT is unique in its ability to provide contextual representations by leveraging Transformer architectures and a masked language model. By using a bidirectional understanding of sentences and training on massive amounts of data, BERT surpassed previous state-of-the-art models in tasks like sentiment analysis and semantic parsing.
The Emergence of ELMo
Before BERT, ELMo made significant strides in contextual representations. By training a language model on a large corpus, ELMo captured sentence-level information and learned to encode the entire sentence's context. The model's embeddings were then used as pre-trained representations for downstream tasks, such as question-answering and semantic parsing.
XLNet: Exploring the Limits of Transfer Learning
XLNet took pre-training models another step forward by introducing advancements like Transformer-XL and permutation language modeling. Transformer-XL addressed the contextual limitations of absolute position embeddings by incorporating relative position embeddings. Permutation language modeling allowed for more efficient pre-training by randomly permuting words during training, resulting in better sample efficiency.
ALBERT: Lite BERT for Self-Supervised Learning
ALBERT aimed to reduce the complexity of pre-trained models while maintaining high performance. By employing factorized embedding tables and cross-layer parameter sharing, ALBERT achieved significant compression in model size while preserving accuracy. These innovations improved sample efficiency and reduced overfitting.
T5: Pushing the Boundaries of Transfer Learning
The T5 model pushed the boundaries of transfer learning by extensively exploring pre-training techniques and fine-tuning approaches. Through careful ablations and experimentation, T5 demonstrated the impact of data size, model size, and pre-training objectives on overall performance.
ELECTRA: A Novel Approach to Pre-training
ELECTRA introduced a unique perspective on pre-training by treating it as