Enhancing Natural Language Processing in Virtual Assistants through Transformer Models
The research question for this annotated bibliography relates to how transformer models can improve natural language processing in virtual assistants. The paper focused on this area because of the increased utilization of virtual assistants in different fields, such as mobile data mining, IoT voice interactions, and the healthcare sector. Below is a list of ten annotated references to aid in fostering the understanding of how natural language processing can be used to enhance virtual assistants through the aid of transformer models. Our assignment writing help is at affordable prices to students of all academic levels and academic disciplines.
Annotated Bibliography
Antona, M., Margetis, G., Ntoa, S., & Degen, H. (2023). Special issue on AI in HCI. International Journal of Human-Computer Interaction, 39(9), 1723-1726. https://doi.org/10.1080/10447318.2023.2177421
This paper brings together research findings highlighting the impact of joining AI and HCI forces. It also proceeds to outline and demonstrate progress in topics related to artificial intelligence and technological solutions to address new and known problems in this vital sector of the economy. Artificial intelligence is already a discipline rich in information and is anticipated to be integrated into most aspects of everyday life. On the other hand, Human-Computer Interaction (HCI) is a field responsible for shaping the development of technology usable to more users. The sixteen papers compiled to form this publication brought together significant topics such as transparency, trust, health and well-being applications, chatbots, human-AI interaction and teaming, and responsible AI.
This article is considered a scholarly reference because it was published on 21st February 2023 as an international Taylor & Francis Group Journal, an academic database that consistently publishes reliable research. The information contained in this article was authored by Margherita Antona, George Margetis, and Stavroula Ntoa from the Foundation for Research and Technology Hellas, Greece. The fourth author of this article is Helmut Degen from Siemens Corporation, Princeton, USA. Therefore, the information in this article is accurate because scholars from the field of research and technology wrote it. This article applies to my research because it touches on vital topics such as AI-based chatbots, human-machine teaming, and Human-Robot Interaction that influence the day-to-day activities of human life.
Bharadiya, J. (2023). A comprehensive survey of deep learning techniques natural language processing. European Journal of Technology, 7(1), 58-66. https://doi.org/10.47672/ejt.1473
Research involving natural language processing has indicated increased attention to unsupervised and semi-supervised learning techniques. These learning techniques can deduce information from annotated and non-annotated data. This article examines the various natural language processing methods, a discipline that integrates computer science, artificial intelligence, and linguistics to foster easy communication between computers and human beings. One of the major challenges that the article tries to address is enabling machines to interpret human language naturally.
This article was published on 23rd May this year. The information in this article is appropriate for my research because it gives room for examining non-annotated data, thus drawing significant insights from them. An expert in the field authored this article – Jasmin Bharatiya, who has a Ph.D. in Information Technology from the University of Cumberland. The article is reliable because it discusses natural language processing methods such as speech recognition, machine translation, morphological separation, part-of-speech tagging, and sentiment analysis. This article applies to my research because it aids in the analysis of a vast amount of data that is in unannotated form, thus increasing the accuracy of the findings.
Bouraoui, A., Jamoussi, S., & Hamadou, A. B. (2022). A comprehensive review of deep learning for natural language processing. International Journal of Data Mining, Modelling and Management, 14(2), 149-182. https://doi.org/10.1504/IJDMMM.2022.123356
Deep learning models focus on comprehending data embeddings with multiple levels of abstraction with many layers of structured or unstructured data. The primary purpose of this article is to provide an evaluation of the evolution of deep learning together with an explanation of various architectures. The information in the article is relevant because it compares the performance of different learning models based on standard datasets related to various MLP tasks. Two broad research trends have been identified as the major components that build higher-level embeddings. On one end, deep learning results in the development of complex models. On the other hand, multi-purpose sentence representations based on simple averages have been identified as being more precise. The article is considered a scholarly reference because it is current and up-to-date since it was published on 3rd June 2022.
The authors of this article are Amal Bouraoui, Salma Jamoussi, and Abdamajid Ben Hamadou. All three authors work at the MIRACL laboratory at Sfax University in Tunisia. The paper is relevant since it is published on an international journal website related to data mining, modelling, and management. The article applies to my research because it is focused on improving deep learning methods through attention strategies.
Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649. https://doi.org/10.1007/978-81-322-3972-7_19
The vast volume of natural language text in the current world has challenged its dissemination. Human beings are experiencing tough times deciphering the wisdom in this huge knowledge volume within limited time frames. Therefore, it has been established that automated natural language processing can effectively execute this function just as an individual would for a limited amount of text. This article aims to examine the challenges of NLP, the progress made thus far, components of NLP, NLP applications, and finally, the grammar of the English language. The article applies to my area of research because it also covers ambiguities, probabilistic parsing, information extraction, discourse analysis, commonsense interfaces, causal diversity, and NL question-answering.
The article is timely for my research because it was published on 5th April 2020. The audience of the information in this article is experts in information technology since it focuses on necessitating comprehension of the machine language by human beings who are the users. This article was written by an expert in this field – Chowdhary, a professor in the Department of Computer Science and Engineering at Jodhpur Institute of Engineering and Technology in India. Therefore, the information in this article is accurate because the author followed all the writing guidelines while compiling this article.
Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172. https://doi.org/10.1080/23270012.2020.1756939
There is increased knowledge of natural language processing (NLP) because it can evaluate and understand human language. Regardless of this extensive application, there needs to be an elaborate review of the literature on how this subject can be applied as an analytical strategy. This article reviews articles published on the Dallas List of 24 LBJ that use NLP as their primary analytical tool for explaining how textual information can advance management models.
The article was published on 14th March 2020 on the Taylor and Francis website. The information in this article is relevant because it increases the readers’ knowledge of ways of understanding NLP. This is a scholarly article written by university professors from different institutions who are experts in the field. For instance, Yue Kang, Qian Huang, and Hefu Liu are professors at the University of Science and Technology of China. Zhai Cai is from the University Of Nottingham, China, while Chee-Wee Tan is from Copenhagen Business School in Denmark. The information is accurate because it is supported by citations from other scholars whose work has been published recently. This article applies to my research because it describes procedural steps for executing NLP as an analytical measure and explains its advantages and disadvantages.
Ni, P., Li, Y., Li, G., & Chang, V. (2020). Natural language understanding approaches are based on the joint task of intent detection and slot filling for IoT voice interaction. Neural Computing and Applications, 32, 16149-16166. https://doi.org/10.1007/s00521-020-04805-x
The IoT-based voice interaction system provides the new human-computer interaction mode. Efficient communication channels have been realized to pose great challenges concerning semantic comprehension of the module within the system. This allows adopting the most powerful NLU. Some of the core sub-tasks of NLU, such as Intent Detection and Slot Filling joint tasks, are utilized to foster semantic comprehension in different incidences. However, the modern era of deep learning has prompted the transition of these tasks from previous roles- to learning-based methods. Generally, this article explores how the models of these two aspects can be refined to improve the intent detection task through increased precision of the Slot Filling task by linking the pre- and post-tasks. This article applies to my research because it performed a detailed comparative analysis of the tasks on various datasets. This article is considered a scholarly reference because it was published in 2020, meaning that it is current and borrowed heavily from the works of other past researchers.
Padmanaban, K., Sathiya, A., Jeevitha, K., & Rosaline, R. A. A. (2023). Human-computer interface challenges in mobile data mining. In 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) (pp. 1661-1666). IEEE. https://doi.org/10.1109/ICSCSS57650.2023.10169161
Systems that permit human-machine communication have experienced significant growth courtesy of developments associated with computer software, hardware, and artificial intelligence. The methods developed in this regard have given room for the arrival of more precise results as opposed to relying on the human mind. NLU forms the core aspect of the human-computer interface. The comprehension of the module’s semantics greatly impacts the success of the Human-Computer Interaction (HCI) framework and the duration it takes for tasks to be executed.
The article is scholarly because it was published on 14th June 2023. It is also relevant to my area of research because it delves deeply into the semantics that influence the success of the Human-Computer Interaction (HCI) framework concerning human-machine communication. The authors are scholars working in different departments in various institutions of higher learning. For instance, Padmanaban works in the Department of Computer Science and Engineering at Koneru Lakshmaiah Education Foundation, while Sathiya works in the Department of Artificial Intelligence and Data Science at Sri Sairam Institute of Technology in India. Jeevitha and Rosaline are also the other authors who worked on this article, and they work at RMK Engineering College and SRM Institute of Science and Technology, respectively. Therefore, the information contained in this article is accurate because it was informed by research from different fields. The content of this article applies to my research because it examines the neural network reasoning structure to bring to light the process of mobile data mining.
Sekaran, K., Chandana, P., Jeny, J. R. V., Meqdad, M. N., & Kadry, S. (2020). Design of optimal search engine using text summarization through artificial intelligence techniques. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(3), 1268-1274. http://doi.org/10.12928/telkomnika.v18i3.14028
As one of the most trending topics, NLP permits developers to actualize human-computer interactions. This discipline combines computer science, artificial intelligence, and computer linguistics. This article explains the design of a search engine with the ability to display the data based on the user’s question and display content the user may be interested in seeing.
This article is scholarly since it was published recently, in 2020. The information in this article is directed toward IT experts or individuals interested in knowing about this vital discipline in modern society. The information contained in this article is relevant because Universitas Ahmad Dahlan published it. The authors established that the designed search engine would experience maximum response time concerning issues affecting the user by evaluating the number of transactions made as inputs. The article applies to my research since the outcomes of the performance analysis indicate text summarization as an efficient way of improving the response time for SEO.
Wang, Z. J., Choi, D., Xu, S., & Yang, D. (2021). Putting humans in the natural language processing loop: A survey. arXiv preprint arXiv: 2103.04044. https://doi.org/10.48550/arXiv.2103.04044
This article primarily focuses on establishing the best way to design NLP systems so they can learn from human feedback. In modern society, a large body of research related to Human-in-the-loop (HITL) NLP frameworks regularly incorporate a human response to foster the model’s outcome. NLP research on HITL has been beneficial to a great extent because it aids in resolving NLP challenges, collecting divergent perspectives from various people, and executing various strategies to learn from the feedback collected.
The article is scholarly because it was published on 6th March 2021. The information in the article relates to my topic since it presents the history of Machine Learning (ML) and Human-Computer Interaction (HCI) communities with a summary that brings to light human interactions, tasks, goals, and feedback on the learning methods. The information contained in this article is accurate and credible because Cornell University published it. The article applies to my research because it brings to light future directions of incorporating human feedback in the NLP development space.
Yang, H., Luo, L., Chueng, L. P., Ling, D., & Chin, F. (2019). Deep learning and its applications to natural language processing. Deep learning: Fundamentals, theory and applications, 89-109. https://doi.org/10.1007/978-3-030-06073-2_4
As a key research area in computer science and artificial intelligence, Natural Language Processing is focused on processing large amounts of data. Despite the application of deep learning technologies in this area, several gaps still exist in this discipline. The two major gaps highlighted were that available research must clarify how deep learning technologies apply to NLP and what relevant applications are expected. This study is focused on investigating the recent development of NLP centred on natural language understanding. The article sets off by exploring newly developed word representation methods. The description of the learning models and recurrent neural networks then follows this.
As a scholarly source, this article was published on 16th February 2019. The intended audience of this article is professionals in the information technology sector or any other party interested in NLP. The article is reliable because it enumerates various key NLP applications, including but not limited to named entity recognition and fundamental NLP applications. This study applies to my research because it presents a series of benchmark datasets useful for evaluating models’ performance in various applications. The information contained in this article is relevant because it was published on the Springer website.
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Question 
Assessment Description
From the attached Word document “Research Paper”, Identify your research question. Refine it from your original question, if needed, based on the research you have completed.
Create an Annotated Bibliography from the 10 references used in your Reference List assignment. Be sure to include an APA-style reference for each article. Each annotation must be 120 words in length and include the following elements:
Enhancing Natural Language Processing in Virtual Assistants through Transformer Models
Each reference includes an annotation that addresses the following:
1. Paraphrased summary of the article
2. Why it is considered a scholarly reference
3. Reflection on how it is applicable to your research
Note on Paraphrasing: Paraphrasing the ideas of others is a requirement in academic writing and graduate study. Paraphrasing is using your own words to restate ideas or information from source material. As you write each annotation, use these three main steps to paraphrase:
1. Identify the original idea(s) in the article.
2. Identify general points regarding the idea(s).
3. Summarize the general points of the article in your own words.