There are several complicated tasks in the Artificial Intelligence domain, and speech recognition is among the most important of them. While reproducing the response to specific actions (what smart devices do) using voice-to-text and text-to-voice modulation is achievable, emulating a high-quality natural speech is still not easy, even for the best Ai models. At the same time, there is a considerable demand for such technology in various areas of production, everyday life, and the economy. That is why NLP has become a technology in demand.
The only loophole for today is the models of the NLP-solutions language design. Such models are used in the design of talking robots, answering machines, bots, dialogue systems, voice assistants, and other conversion services.
Among widespread solutions are BERT, GPT-3, Roberta, CodeBERT, ALBERT, XLNet, StructBERT, T5, ELECTRA, DeBERTa, and GPT-4.
Below are the most critical NLP language models that are already present on the international market today.
BERT (Bidirectional encoder representation from transformers) is a question-answering language model created by Google in 2018. It is often used to perform various tasks. This allows for exceptional communications in as little as an hour. The model is fully trainable and understandable.
The BERT framework: It’sIt’s an unsupervised bi-directional system for pre-training NLP models. Bi-directional means detecting and revealing text (left-to-right and right-to-left). Unsupervised means the absence of programs and algorithms that must be performed according to a given scenario.
The product was developed on Wikipedia’sWikipedia’s plain text corpus. This approach shows better recognition and “understanding” of the context by product. After the initial tests, BERT was recognized as the “new era of NLP” as it successfully completed 11 tasks.
Among the advantages of BERT for business are the following:
Customer service bots;
Customer feedback processing;
GPT-3 by OpenAI
The OpenAI’sOpenAI’s GPT-3 language model is an update to previous versions. It is known as revolutionary because it has worked for as many as 175B parameters. No other earlier model can boast of such indicators.
GPT-3 did an excellent job while being tested on various NLP tasks, including speech recognition, translation, answering questions, and news generation. The latter impressed even the most inveterate skeptics since it was believed that only a human could generate a high-quality text.
The texts from the Internet were taken as the basis for those 175 billion parameters, and their total volume was 45 TB.
The most important feature of the solution is reprogramming opportunities.
The following offers are among the benefits of GPT-3 for business:
Automated translation and documents processing;
Programming without code;
Creation of tests and quizzes.
RoBERTa (Robustly Optimized BERT Pre-Training Approach)
RoBERTa is an optimized solution for self-supervising pre-trained NLP systems developed by Facebook. The essential function of the product is the recognition of certain sections of text according to the specified parameters. For example, searching for obscene language references to prohibited topics or entries in an unfriendly tone.
RoBERTa is a kind of “improver” of the BERT model because it better processes the hidden language and thus allows you to achieve higher product performance. For example, the solution is suitable for working with extensive criteria packages and does not require preliminary training for every single task.
The benefits of RoBERTa and similar solutions are proven when compared to the GLUE benchmark.
Among the key advantages of RoBERTa for business are the following offers:
Audio/text chat options;
CodeBERT is another language model based on a pre-trained algorithm. The developer is a Microsoft company. The solution is designed to work in natural (NL) and programming (PL) language: search and recognition of data in human language, with its subsequent transformation into code. It is the most commonly used feature for creating web documents.
The advantage of this solution is that it works optimally with NL and PL. This has been proven by testing on a GitHub’s GitHub’s large amount of data, which includes six programming languages:
ALBERT is another creative solution from Google. Those who don’t want to work with overly fancy models or those who don’t need them can rely on this product. Its advantage is that it is easy to use like BERT but handles more tasks and parameters just fine like pre-trained models.
The relative disadvantage of the solution is that such a combination can potentially lead to higher training time and contains a limit on the GPU / TPU memory volume.
However, the developers have done their best to prevent such problems with the following iterations:
Separation of hidden layers from the vocabulary inclusions (Factorized embedding parametrization).
Preventing the parameters’ growth as the depth of the network increases (Cross-layer parameter separation).
Dozen of iterations were carried out based on NLP tasks, and the final product appeared in the TensorFlow framework form. As a result, a reduction in the number of criteria by 89% was achieved, and the average accuracy of the processes was increased to 80.1%. The benchmark for indicators’ evaluation was the BERT model.
Among the advantages of ALBERT for business are such offers as:
Chatbots design improvement;
Variety of algorithm-based NLP-models
Generally, the average developer does not undertake to create a language model from scratch. This is due to limited resources (time, budget, software). However, the high demand for this type of solution suggests that there should be more template models, and advanced specialists should focus on their development. In addition, iteration of existing models allows you to save resources and achieve better results in shorter periods than creating a product from scratch.