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Improving astroBERT using Semantic Textual Similarity

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arxiv 2212.00744 v1 pith:SFHYIVW5 submitted 2022-11-29 cs.CL astro-ph.IM

Improving astroBERT using Semantic Textual Similarity

Felix Grezes , Thomas Allen , Sergi Blanco-Cuaresma , Alberto Accomazzi , Michael J. Kurtz , Golnaz Shapurian , Edwin Henneken , Carolyn S. Grant
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Donna M. Thompson Timothy W. Hostetler Matthew R. Templeton Kelly E. Lockhart Shinyi Chen Jennifer Koch Taylor Jacovich Pavlos Protopapas
This is my paper
classification cs.CL astro-ph.IM
keywords astrobertlanguageastrophysicsastronomycitationmodelpublicscientific
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to the text used in astronomy papers in ADS. In this work we: - announce the first public release of the astroBERT language model; - show how astroBERT improves over existing public language models on astrophysics specific tasks; - and detail how ADS plans to harness the unique structure of scientific papers, the citation graph and citation context, to further improve astroBERT.

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