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 to sentence-level aspects of
documents, and present two methods for aspect matching: (1) A fast method that
only matches single aspects, and (2) a method that makes sparse multiple
matches with an Optimal Transport mechanism that computes an Earth Mover's
Distance between aspects. Our approach improves performance on document
similarity tasks in four datasets. Further, our fast single-match method
achieves competitive results, paving the way for applying fine-grained
similarity to large scientific corpora. Code, data, and models available at:
this https URL

通过匹配文本的精细细节，我们提出了一种新的科学文献相似性模型。我们利用多个论文引用的全文来训练我们的模型，提供了一种新形式的文本监督用于跨论文匹配，具有快速单次匹配和稀疏多次匹配两种方法，提高了在四个数据集上的相似性任务表现。

使用文本引导的多向量模型用于精细化科学文档相似性

Multi-Vector Models with Textual Guidance for Fine-Grained Scientific  Document Similarity

This document describes the details and the motivation behind a new dataset
we collected for the semi-supervised recognition challenge~\cite{semi-aves} at
the FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds
sampled from the iNat-2018 dataset for a total of nearly 150k images. From this
collection, we sample a subset of classes and their labels, while adding the
images from the remaining classes to the unlabeled set of images. The presence
of out-of-domain data (novel classes), high class-imbalance, and fine-grained
similarity between classes poses significant challenges for existing
semi-supervised recognition techniques in the literature. The dataset is
available here: https://github.com/cvl-umass/semi-inat-2020

本文介绍了一个新的半监督识别数据集，该数据集包含来自 iNat-2018 数据集中抽样得到的 1000 种鸟类，总共近 15 万张图像。该数据集的存在挑战着现有文献中的半监督识别技术，因为它包含领域外数据、类不平衡和类之间的微妙相似性。