Bort: A More Efficient Variant of BERT Architecture
Bort is a superior architectural variant of BERT, an effective neural network for natural language processing. The idea behind Bort is to optimize the subset of architectural parameters for the BERT architecture via a fully polynomial-time approximation scheme (FPTAS) by fully utilizing the power of neural architecture search.
Among neural networks, BERT is one of the most effective because it is pre-trained for on a massive amount of text data. By being pre-trained on a huge amount of text data, BERT is then able to perform various NLP tasks like sentiment analysis, text classification, and translation. While the performance of BERT is impressive, researchers found that the structure of this neural network can be optimized further, resulting in an architecture that is more efficient and streamlined, but just as effective.
Optimizing BERT Architecture Via Neural Architecture Search
Initially, researchers hypothesized that BERT's overall success is due to the optimal combination of architectural parameters in its configuration. Therefore, by fine-tuning and reducing the number of parameters, they could create a more efficient and effective network than BERT.
Generally speaking, the number of parameters in a neural network can directly affect its operability and computation time, among other things. For this reason, in designing Bort, researchers aimed to produce a setup that achieved equally good performance with a significantly smaller number of parameters. To achieve this, they used a technique called neural architecture search (NAS), which is an advanced method of optimizing neural network architecture.
Within the context of NAS, the initial step to creating Bort was to determine which architectural parameters of BERT are essential for its fantastic performance. Afterward, FPTAS is utilized to search for an optimal subset of these essential parameters. This method, when applied to BERT, then led to a more efficient neural network known as Bort.
The Benefits of Bort
One of the primary benefits of Bort lies in its size. It is demonstrably smaller than BERT and requires about $5.5\%$ of its large architecture and $16\%$ of the full net size. Not only does this smaller structure require less computation and memory power, but it also renders it much more accessible to those who would like to use the model. As a result, in terms of practicality, Bort offers a significant advantage over the previous BERT variant.
Another benefit of Bort is that it can be pre-trained in $288$ GPU hours. This is $1.2\%$ less than the time required to pre-train the leading BERT variant called RoBERTa-large. Pre-training time refers to the time it takes to train a model on vast databases of information, so this relates back to how efficiently Bort operates in comparison to traditional BERT models.
Implications of Bort
The development of Bort has many practical implications for the field of natural language processing. The most obvious is that Bort can be utilized by researchers who need more capable models in text-based machine learning applications. Bort also offers a path for researchers who would like to create similar models with a smaller size but with similar performance.
Bort is also an excellent aid to those working on edge devices with limited computing power or memory. The need for this technology is growing with the increasing prevalence of IoT devices, voice assistants, and smart home systems. Such devices must impart AI capabilities without straining their computational resources; using efficient models such as Bort remains an attractive proposition for many reasons.
In short, Bort is an efficient neural network that builds upon the already powerful BERT architecture. Through a novel neural architecture search approach, Bort optimizes the subset of critical parameters of BERT architecture to create an optimal network subset that is demonstrably smaller than previous variants. As a result of Bort, the NLP community now has access to more streamlined and efficient models that can perform equally well, if not better than previous models.
With more research into NAS and other potential variations of BERT, the potential advancements in natural language processing technology could be exponential. An efficient pre-trained model like Bort opens the door for exciting new research that could lead to significant technological breakthroughs in the field of natural language processing.
