Computer-assisted Revision in Spanish Academic Texts: Peer-assessment

Ferrero, Carmen López; Renau, Irene; Nazar, Rogelio; Torner, Sergi

doi:10.1016/j.sbspro.2014.05.083

Cited by 7 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to standard augmentation techniques, authors achieved 10% and 5% relative performance improvement on IEMOCAP and FEEL-25k, respectively. Fu et al [78] designed an adversarial autoencoder (AAEC) emotional classifier, through which the dataset was expanded in order to improve the robustness and generalisation of the classifier. The proposed model generated most of the new samples almost within the real distribution.…”

Section: Data Augmentationmentioning

confidence: 99%

Human Computer Interaction for Intelligent Systems

2023

View full text Add to dashboard Cite

show abstract

Section: Data Augmentationmentioning

confidence: 99%

Human Computer Interaction for Intelligent Systems

2023

View full text Add to dashboard Cite

show abstract

“…Lopez-Ferrero et al (2014) presented experiments in automatic correction of spelling and grammar errors in Spanish academic texts with the goal of developing a tool to assist university students in writing academic texts Ondas et al (2015). focused on the linguistics analysis of written or spoken Slovak texts which can contribute to learning activities in the local context.…”

mentioning

confidence: 99%

core technology behind and beyond ChatGPT

Leong,

Sung,

Jones

2023

ijeri

View full text Add to dashboard Cite

ChatGPT has garnered significant attention within the education industry. Given the core technology behind ChatGPT is language model, this study aims to critically review related publications and suggest future direction of language model in educational research. We aim to address three questions: i) what is the core technology behind ChatGPT, ii) what is the state of knowledge of related research and iii) the potential research direction. A critical review of related publications was conducted in order to evaluate the current state of knowledge of language model in educational research. In addition, we further suggest a purpose oriented guiding framework for future research of language model in education. Our study promptly responded to the concerns raised by ChatGPT from the education industry and offers the industry with a comprehensive and systematic overview of related technologies. We believe this is the first time that a study has been conducted to systematically review the state of knowledge of language model in educational research.

show abstract