Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity. (arXiv:2111.08366v3 [cs.CL] UPDATED)

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

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 …

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