Improve SQuAD fine-tuned ELECTRA model on Adversarial QA

Improve SQuAD fine-tuned ELECTRA model on Adversarial QA

Registration ID: IJNRD_213903

Published ID: IJNRD2402269

DOI: Click Here to Get

Gunjan Srivastava

To evaluate the performance of model, our primary approach has been to measure accuracy of model on validation dataset. Benchmark datasets used to train and validate NLP models may often overestimate the performance. These observations all stem from the fact that a model may achieve high performance on a dataset by learning spurious correlations, also called dataset artifacts. The model is then expected to fail in settings where these artifacts are not present, which may include real-world testbeds of interest. Our aim in this paper is to evaluate pre-trained QA model fine-tuned on SQuAD benchmark dataset and rigorously test it against challenging dataset; identify different types of issues in the model; and further define an approach to improve the model performance on one of the specified issues.

Published Paper   E-Certificate

"Improve SQuAD fine-tuned ELECTRA model on Adversarial QA", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 2, page no.c623-c628, February-2024, Available :https://ijnrd.org/papers/IJNRD2402269.pdf

Volume 9 Issue 2, February-2024

Pages : c623-c628

Paper Reg. ID: IJNRD_213903

Published Paper Id: IJNRD2402269

Downloads: 000121136

Research Area: Engineering

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2402269.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2402269

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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