Overview of ROBERTa model

Introduction:

RoBERTa (short for “Robustly Optimized BERT Approach”) is a variant of the BERT (Bidirectional Encoder Representations from Transformers) model, which was developed by researchers at Facebook AI. Like BERT, RoBERTa is a transformer-based language model that uses self-attention to process input sequences and generate contextualized representations of words in a sentence.

One key difference between RoBERTa and BERT is that RoBERTa was trained on a much larger dataset and using a more effective training procedure. In particular, RoBERTa was trained on a dataset of 160GB of text, which is more than 10 times larger than the dataset used to train BERT. Additionally, RoBERTa uses a dynamic masking technique during training that helps the model learn more robust and generalizable representations of words.

RoBERTa has been shown to outperform BERT and other state-of-the-art models on a variety of natural language processing tasks, including language translation, text classification, and question answering. It has also been used as a base model for many other successful NLP models and has become a popular choice for research and industry applications.

Overall, RoBERTa is a powerful and effective language model that has made significant contributions to the field of NLP and has helped to drive progress in a wide range of applications.

RoBERTa stands for Robustly Optimized BERT Pre-training Approach. It was presented by researchers at Facebook and Washington University. The goal of this paper was to optimize the training of BERT architecture in order to take lesser time during pre-training.

Modifications to BERT:

RoBERTa has almost similar architecture as compare to BERT, but in order to improve the results on BERT architecture, the authors made some simple design changes in its architecture and training procedure. These changes are:

• Removing the Next Sentence Prediction (NSP) objective:  In the next sentence prediction, the model is trained to predict whether the observed document segments come from the same or distinct documents via an auxiliary Next Sentence Prediction (NSP) loss. The authors experimented with removing/adding of NSP loss to different versions and concluded that removing the NSP loss matches or slightly improves downstream task performance
• Training with bigger batch sizes & longer sequences: Originally BERT is trained for 1M steps with a batch size of 256 sequences. In this paper, the authors trained the model with 125 steps of 2K sequences and 31K steps with 8k sequences of batch size. This has two advantages, the large batches improves perplexity on masked language modelling objective and as well as end-task accuracy. Large batches are also easier to parallelize via distributed parallel training.
• Dynamically changing the masking pattern: In BERT architecture, the masking is performed once during data preprocessing, resulting in a single static mask. To avoid using the single static mask, training data is duplicated and masked 10 times, each time with a different mask strategy over 40 epochs thus having 4 epochs with the same mask. This strategy is compared with dynamic masking in which different masking is generated  every time we pass data into the model.

Datasets Used:

The following are the datasets used to train ROBERTa model:

• BOOK CORPUS and English Wikipedia dataset: This data also used for training BERT architecture, this data contains 16GB of text.
• CC-NEWS. This data contains 63 million English news articles crawled between September 2016 and February 2019. The size of this dataset is 76 GB after filtering.
• OPENWEBTEXT: This dataset contains web content extracted from the URLs shared on Reddit with at least 3 upvotes. The size of this dataset is 38 GB.
• STORIES: This dataset contains a subset of Common Crawl data filtered to match the story-like style of Winograd NLP task. This dataset contains 31 GB of text.

Results:

• On the GLUE benchmark NLP tasks, the model achieves a score of 88.5 on the public leaderboard and achieve the state-of-the-art score on 4 of GLUE tasks: Multi Natural Language Inference (MNLI), QuestionNLI, Semantic Textual Similarity Benchmark (STS-B), and Recognizing Textual Entailments (RTE) at the time of its release.
• At the time of its release, On the SQuAD 1.1 and SQuAD 2.0 datasets, it is able to match the previous state-of-the-Art results by XLNet.
• It also achieves better results than BERT(LARGE) model and XLNet on RACE benchmark datasets.

References:

• Facebook AI blog
• ROBERTa paper

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