Web: http://arxiv.org/abs/2111.08366

May 5, 2022, 1:11 a.m. | Sheshera Mysore, Arman Cohan, Tom Hope

cs.CL updates on arXiv.org arxiv.org

We present a new scientific document similarity model based on matching
fine-grained aspects of texts. To train our model, we exploit a
naturally-occurring source of supervision: sentences in the full-text of papers
that cite multiple papers together (co-citations). Such co-citations not only
reflect close paper relatedness, but also provide textual descriptions of how
the co-cited papers are related. This novel form of textual supervision is used
for learning to match aspects across papers. We develop multi-vector
representations where vectors correspond …

arxiv guidance models vector

More from arxiv.org / cs.CL updates on arXiv.org

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California