English grammar multiple-choice question generation using Text-to-Text Transfer Transformer

Chomphooyod, Peerawat; Suchato, Atiwong; Tuaycharoen, Nuengwong; Punyabukkana, Proadpran

doi:10.1016/j.caeai.2023.100158

Cited by 2 publications

(2 citation statements)

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“…Dialog summarization for software collaborative platform via tuning pre-trained models [250]. English grammar multiple-choice question generation using Text-to-Text Transfer Transformer [248]. Collectively Text-to-Text Transfer Transformers (T5) these studies underscore the immense potential of transformer models, including Text-to-Text Transfer Transformers, in advancing the fields of information extraction, comprehension, and generation across diverse domains.…”

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

“…Paraphrasing techniques are a linchpin in recognizing or generating expressions and sentences with akin meanings. With a keen focus on generating coherent and semantically akin paraphrases, this model, powered by a finelytuned Text-To-Text Transfer Transformer (T5) [244], [248], [250], [260], [165], [223], [290], [291], [305] XLNet Personality is a complex mix of traits that define how we think, act, and feel, and it's studied in various fields like healthcare and social media. smart computer models like XLNet to predict personality traits from text, making it more accurate and helpful.…”

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

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Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Suryanto,

Wibawa,

Hariyono

et al. 2023

IJRCS

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Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.

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Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%