Advanced BERT Variants: ALBERT, RoBERTa, ELECTRA, and SpanBERT

This document provides a summary of advanced BERT variants, focusing on their architectural innovations, pre-training strategies, and key improvements over the original BERT model.

1. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

ALBERT (A Lite BERT) is designed to significantly reduce the number of parameters compared to BERT, leading to more efficient training and inference without sacrificing performance. It achieves this through two primary parameter reduction techniques:

• Cross-layer Parameter Sharing: ALBERT reuses the same set of parameters (weights) across multiple Transformer layers. This drastically reduces the total number of trainable parameters.
  • Shared Feedforward and Attention: ALBERT allows for sharing of either the feedforward layers, the self-attention layers, or both, across all Transformer blocks.
• Factorized Embedding Parameterization: The large embedding matrix in BERT is factorized into two smaller matrices. This decouples the size of the hidden layers from the size of the vocabulary embedding.

Additionally, ALBERT introduces a change to the pre-training objectives:

• Sentence Order Prediction (SOP): ALBERT replaces BERT's Next Sentence Prediction (NSP) task with the SOP task. SOP is a binary classification task where the model predicts whether two input sentences are in their original order or have been swapped. This is designed to improve the model's understanding of inter-sentence coherence, as it's a more challenging task than simply predicting whether one sentence follows another.

2. RoBERTa: A Robustly Optimized BERT Pretraining Approach

RoBERTa (A Robustly Optimized BERT Pretraining Approach) is an optimized version of BERT that focuses on improving training methodology and data processing. Key differences include:

• Masked Language Modeling (MLM) Only: RoBERTa trains using only the MLM objective, dropping the NSP task.
• Dynamic Masking: Instead of statically masking tokens once during data preprocessing, RoBERTa implements dynamic masking. This means the masking pattern is generated every time a sequence is fed to the model, leading to more varied training signals.
• Larger Batch Sizes and More Data: RoBERTa is trained with significantly larger batch sizes and on a much larger dataset than BERT, contributing to its robust performance.
• Byte-Pair Encoding (BPE): RoBERTa utilizes Byte-Pair Encoding (BPE) with a larger vocabulary size (50,000 tokens). This subword tokenization strategy can handle a wider range of words and characters more effectively.

3. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

ELECTRA introduces a novel and computationally efficient pre-training task called Replaced Token Detection. Instead of masking tokens, ELECTRA uses a generator-discriminator setup:

• Generator: A small masked language model (like BERT) masks some tokens and then predicts plausible replacements for them.
• Discriminator: The main ELECTRA model is trained as a discriminator. It receives sequences where some tokens have been replaced by the generator's predictions. The discriminator's task is to identify whether each token in the sequence is "original" or "replaced."

This approach is more efficient because the discriminator is trained on every token in the input, unlike MLM where it only learns from the masked tokens. This leads to better performance on downstream tasks with less computational cost.

4. SpanBERT: Improving Pre-training by Representing and Predicting Spans

SpanBERT is an extension of BERT specifically designed to improve performance on span-based tasks, such as question answering and coreference resolution. It modifies BERT's pre-training in the following ways:

• Masking Contiguous Spans: Instead of masking random individual tokens, SpanBERT masks contiguous spans of tokens. For example, it might mask "New York City" as a single unit.
• Span Boundary Objective (SBO): A new pre-training task called SBO is introduced. This objective aims to predict the masked span using only the representations of the tokens at the boundaries of the span and the positions of the masked tokens. It leverages the surrounding context to reconstruct the masked span. This encourages the model to learn richer representations of spans.

Knowledge Check: Questions and Answers

To solidify your understanding, consider the following:

• How does the SOP task differ from the NSP task? The SOP (Sentence Order Prediction) task determines whether two sentences are in the correct order relative to each other. This is distinct from NSP (Next Sentence Prediction), which checks if the second sentence naturally follows the first in the original document. SOP is considered a more challenging task that better captures inter-sentence coherence.

• What are the parameter reduction techniques used in ALBERT? ALBERT uses factorized embedding parameterization and cross-layer parameter sharing to significantly reduce the number of model parameters and improve training efficiency.

• What is cross-layer parameter sharing? Cross-layer parameter sharing is a technique where the same set of parameters is reused across multiple layers of the neural network. In ALBERT's context, this means the weights for the self-attention and feedforward modules are shared across all Transformer blocks.

• What are the shared feedforward and shared attention options in cross-layer parameter sharing? ALBERT allows for sharing either the feedforward layers, the self-attention layers, or both, across all Transformer blocks. This provides flexibility in how parameter reduction is applied.

• How does RoBERTa differ from the BERT model? RoBERTa eliminates the NSP task, adopts dynamic masking (instead of static), uses larger batch sizes, is trained on more data, and employs Byte-Pair Encoding (BPE) with a larger vocabulary.

• What is the replaced token detection task in ELECTRA? In ELECTRA, the replaced token detection task involves a generator model replacing masked tokens with plausible but incorrect ones. The main ELECTRA model (the discriminator) is then trained to identify which tokens in the sequence have been replaced.

• How do we mask tokens in SpanBERT? SpanBERT masks contiguous spans of tokens, rather than individual tokens. The model then learns to predict the masked span using contextual cues and representations of the span's boundary tokens.

Further Reading and Research Papers

To deepen your understanding of these BERT variants, refer to the original research papers:

• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations Zhenzhong Lan et al. Read the paper

• RoBERTa: A Robustly Optimized BERT Pretraining Approach Yinhan Liu, Myle Ott, et al. Read the paper

• ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators Kevin Clark et al. Read the paper

• SpanBERT: Improving Pre-training by Representing and Predicting Spans Mandar Joshi et al. Read the paper