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Artificial Judgement Assistance from teXt (AJAX): Applying Open Domain Question Answering to Nuclear Non-proliferation Analysis

ESARDA Bulletin - The International Journal of Nuclear Safeguards and Non-Proliferation

Details

Identification
ISSN: 1977-5296, DOI: 10.3011/ESARDA.IJNSNP.2021.10
Publication date
1 December 2021
Author
Joint Research Centre

Description

Volume: 63, December 2021, pages 41-46,
Special Issue on Data Analytics for Safeguards and Non-Proliferation

Authors: Benjamin Wilson, Kayla Duskin, Megha Subramanian, Rustam Goychayev and Alejandro Michel Zuniga

Pacific Northwest National Laboratory

Abstract:

Nuclear non-proliferation analysis is complex and subjective, as the data is sparse, and examples are rare and diverse. While analysing non-proliferation data, it is often desired that the findings be completely auditable such that any claim or assertion can be sourced directly to the reference material from which it was derived. Currently this is accomplished by analysts thoroughly documenting underlying assumptions and clearly referencing details to source documents. This is a labour-intensive and time- consuming process that can be difficult to scale with geometrically increasing quantities of data. In this work, we describe an approach to leverage bi-directional language models for nuclear non-proliferation analysis. It has been shown recently that these models not only capture language syntax but also some of the relational knowledge present in the training data. We have devised a unique Salt and Pepper strategy for testing the knowledge present in the language models, while also introducing auditability function in our pipeline. We demonstrate that fine-tuning the bi-directional language models on domain specific corpus improves their ability to answer domain-specific factoid questions. Our hope is that the results presented in this paper will further the natural language processing (NLP) field by introducing the ability to audit the answers provided by the language models to bring forward the source of said knowledge.

Keywords: natural language processing, open domain question answering, bi-directional language models, nuclear proliferation detection

Reference guideline:

Wilson, B., Duskin, K., Subramanian, M., Goychayev, R., & Michel Zuniga, A. (2021). Artificial Judgement Assistance from teXt (AJAX): Applying Open Domain
Question Answering to Nuclear Non-proliferation Analysis. ESARDA Bulletin - The International Journal of Nuclear Safeguards and Non-Proliferation, 63, 41-46.
https://doi.org/10.3011/ESARDA.IJNSNP.2021.10

Thumb article Bulletin 63, p.41-46

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Artificial Judgement Assistance from teXt (AJAX): Applying Open Domain Question Answering to Nuclear Non-proliferation Analysis
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