A Review on BERT and Its Implementation in Various NLP Tasks (original) (raw)
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A Primer in BERTology: What We Know About How BERT Works
Transactions of the Association for Computational Linguistics, 2020
Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it learns and how it is represented, common modifications to its training objectives and architecture, the overparameterization issue, and approaches to compression. We then outline directions for future research.
BERT Algorithm used in Google Search
Mathematical Statistician and Engineering Applications
Search engines are now a need for obtaining information due to the internet's explosive expansion in digital material. One of the most widely used search engines, Google, works hard to improve its search functionality. Google has recently used cutting-edge natural language processing (NLP) methods to enhance search results. The Bidirectional Encoder Representations from Transformers (BERT) method is one such ground-breaking invention. This study seeks to offer a thorough evaluation of the BERT algorithm and its use in Google Search. We examine BERT's design, training procedure, and salient characteristics, emphasising its capacity to comprehend the subtleties and context of real language. We also talk about BERT's effects on user experience and search engine optimisation (SEO), as well as potential future advances and difficulties.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
In this digital era, Natural language Processing is not just a computational process rather it is a way to communicate with machines as humanlike. It has been used in several fields from smart artificial assistants to health or emotion analyzers. Imagine a digital era without Natural language processing is something which we cannot even think of. In Natural language Processing, firstly it reads the information given and after that begins making sense of the information. After the data has been properly processed, the real steps are taken by the machine throwing some responses or completing the work. In this paper, I review the journey of natural language processing from the late 1940s to the present. This paper also contains several salient and most important works in this timeline which leads us to where we currently stand in this field. The review separates four eras in the history of Natural language Processing, each marked by a focus on machine translation, artificial intelligence impact, the adoption of a logico-grammatical style, and an attack on huge linguistic data. This paper helps to understand the historical aspects of Natural language processing and also inspires others to work and research in this domain.
Exploring the Role of Transformers in NLP: From BERT to GPT-3
IRJET, 2023
The paper "Exploring the Role of Transformers in NLP: From BERT to GPT-3" provides an overview of the role of Transformers in NLP, with a focus on BERT and GPT-3. It covers topics such as the Role of Transformers in BERT, Transformer Encoder Architecture BERT, and Role of Transformers in GPT-3, Transformers in GPT-3 Architecture, Limitations of Transformers, Transformer Neural Network Design, and Pre-Training Process. The paper also discusses attention visualization and future directions for research, including developing more efficient models and integrating external knowledge sources. It is a valuable resource for researchers and practitioners in NLP, particularly the attention visualization section
Lessons Learned from Applying off-the-shelf BERT: There is no Silver Bullet
2020
One of the challenges in the NLP field is training large classification models, a task that is both difficult and tedious. It is even harder when GPU hardware is unavailable. The increased availability of pre-trained and off-the-shelf word embeddings, models, and modules aim at easing the process of training large models and achieving a competitive performance. We explore the use of off-the-shelf BERT models and share the results of our experiments and compare their results to those of LSTM networks and more simple baselines. We show that the complexity and computational cost of BERT is not a guarantee for enhanced predic-tive performance in the classification tasks at hand.
Improving the BERT Model with Proposed Named Entity Recognition Method for Question Answering
6th International Conference on Computer Science and Engineering (UBMK), 2021
Recently, the analysis of textual data has gained importance due to the increase in comments made on web platforms and the need for ready-made answering systems. Therefore, there are many studies in the fields of natural language processing such as text summarization and question answering. In this paper, the accuracy of the BERT language model is analyzed for the question answering domain, which allows to automatically answer a question asked. Using SQuAD, one of the reading comprehension datasets, the answers to the questions that the BERT model cannot answer are researched with the proposed Named Entity Recognition method in natural language processing. The accuracy of BERT models used with the proposed Named Entity Recognition method increases between 1.7% and 2.7%. As a result of the analysis, it is shown that the BERT model doesn't use Named Entity Recognition technique sufficiently.
TiltedBERT: Resource Adjustable Version of BERT
2022
In this paper, we proposed a novel adjustable fine-tuning method that improves the training and inference time of the BERT model on downstream tasks. In the proposed method, we first detect more important word vectors in each layer by our proposed redundancy metric and then eliminate the less important word vectors with our proposed strategy. In our method, the word vector elimination rate in each layer is controlled by the Tilt-Rate hyper-parameter, and the model learns to work with a considerably lower number of Floating Point Operations (FLOPs) than the original BERT\textsubscript{base} model. Our proposed method does not need any extra training steps, and also it can be generalized to other transformer-based models. We perform extensive experiments that show the word vectors in higher layers have an impressive amount of redundancy that can be eliminated and decrease the training and inference time. Experimental results on extensive sentiment analysis, classification and regressi...
How Can BERT Help Lexical Semantics Tasks?
arXiv (Cornell University), 2019
Contextualized embeddings such as BERT can serve as strong input representations to NLP tasks, outperforming their static embeddings counterparts such as skip-gram, CBOW and GloVe. However, such embeddings are dynamic, calculated according to a sentencelevel context, which limits their use in lexical semantics tasks. We address this issue by making use of dynamic embeddings as word representations in training static embeddings, thereby leveraging their strong representation power for disambiguating context information. Results show that this method leads to improvements over traditional static embeddings on a range of lexical semantics tasks, obtaining the best reported results on seven datasets.
An Extensive Analysis Between Different Language Models: GPT-3, BERT and MACAW
The increase in human interactions with commercial applications has given rise to the demand for Interactive interfaces like chat- bots, text translators, text predictors, and text generators which use pre-trained Language Models to perform their own specific tasks. Language models are leading-edge technologies that enable machines to read, decode, comprehend, and make sense of human languages and respond in appropriate ways. In this paper GPT-3, BERT and Macaw language models are tested on different categorial questions to understand their architecture and behaviour in various circumstances. GPT-3 being pre-trained on a robust dataset gives very elaborate and human like answers, while the outputs produced by BERT can be customised by providing custom context and on the other hand Macaw shows more accuracy while answering to general questions.
Improving the BERT model for long text sequences in question answering domain
International Journal of Advances in Applied Sciences (IJAAS), 2023
The text-based question-answering (QA) system aims to answer natural language questions by querying the external knowledge base. It can be applied to real-world systems like medical documents, research papers, and crime-related documents. Using this system, users don't have to go through the documents manually the system will understand the knowledge base and find the answer based on the text and question given to the system. Earlier state-of-the-art natural language processing (NLP) was recurrent neural network (RNN) and long short-term memory (LSTM). As a result, these models are hard to parallelize and poor at retaining contextual relationships across long text inputs. Today, bidirectional encoder representations from transformers (BERT) are the contemporary algorithm for NLP. BERT is not capable of handling long text sequences; it can handle 512 tokens at a time which makes it difficult for long context. Smooth inverse frequency (SIF) and the BERT model will be incorporated together to solve this challenge. BERT trained on the Stanford question answering dataset (SQuAD) and SIF model demonstrates robustness and effectiveness on long text sequences from different domains. Experimental results suggest that the proposed approach is a promising solution for QA on long text sequences.