Investigating Paraphrasing-Based Data Augmentation for Task-Oriented Dialogue Systems

Vogel, Liane; Flek, Lucie

doi:10.1007/978-3-031-16270-1_39

Cited by 1 publication

(3 citation statements)

References 17 publications

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“…The study demonstrates how these models can effectively generate paraphrased template phrases, significantly reducing the need for manually annotated training data while maintaining or even improving the performance of a natural language understanding (NLU) system [70]. Shuohua Zhou and Yanping Zhang focus on improving medical question-answering systems [71]. It employs a combination of BERT, GPT-2, and T5-Small models, leveraging GPT-2 for question augmentation and T5-Small for topic extraction [71].…”

Section: Existing Research On Gpt's Use In Research Datamentioning

confidence: 99%

“…Shuohua Zhou and Yanping Zhang focus on improving medical question-answering systems [71]. It employs a combination of BERT, GPT-2, and T5-Small models, leveraging GPT-2 for question augmentation and T5-Small for topic extraction [71]. The approach demonstrates enhanced prediction accuracy, showcasing the model's potential in medical question-answering and generation tasks.…”

Section: Existing Research On Gpt's Use In Research Datamentioning

confidence: 99%

“…Medical and Healthcare: Describes the use of GPT models for generating and augmenting data in the medical field, such as patient records, to improve healthcare outcomes and NLP tasks. Research works in [11,40,[53][54][55]69,71,73] could be directly attributed to this sub-category.…”

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confidence: 99%

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Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

Sufi

2024

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GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitating idea generation, enhancing drafting processes, and overcoming challenges like writer’s block. Their capabilities extend beyond conventional applications, contributing to critical analysis, data augmentation, and research design, thereby elevating the efficiency and quality of scholarly endeavors. Strategically narrowing its focus, this review explores alternative dimensions of GPT and LLM applications, specifically data augmentation and the generation of synthetic data for research. Employing a meticulous examination of 412 scholarly works, it distills a selection of 77 contributions addressing three critical research questions: (1) GPT on Generating Research data, (2) GPT on Data Analysis, and (3) GPT on Research Design. The systematic literature review adeptly highlights the central focus on data augmentation, encapsulating 48 pertinent scholarly contributions, and extends to the proactive role of GPT in critical analysis of research data and shaping research design. Pioneering a comprehensive classification framework for “GPT’s use on Research Data”, the study classifies existing literature into six categories and 14 sub-categories, providing profound insights into the multifaceted applications of GPT in research data. This study meticulously compares 54 pieces of literature, evaluating research domains, methodologies, and advantages and disadvantages, providing scholars with profound insights crucial for the seamless integration of GPT across diverse phases of their scholarly pursuits.

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Section: Existing Research On Gpt's Use In Research Datamentioning

confidence: 99%

Section: Existing Research On Gpt's Use In Research Datamentioning

confidence: 99%