Existing Machine Learning approaches for local citation recommendation
directly map or translate a query, which is typically a claim or an entity
mention, to citation-worthy research papers. Within such a formulation, it is
challenging to pinpoint why one should cite a specific research paper for a
particular query, leading to limited recommendation interpretability. To
alleviate this, we introduce the evidence-grounded local citation
recommendation task, where the target latent space comprises evidence spans for
recommending specific papers. Using a distantly-supervised evidence retrieval
and multi-step re-ranking framework, our proposed system, ILCiteR, recommends
papers to cite for a query grounded on similar evidence spans extracted from
the existing research literature. Unlike past formulations that simply output
recommendations, ILCiteR retrieves ranked lists of evidence span and
recommended paper pairs. Secondly, previously proposed neural models for
citation recommendation require expensive training on massive labeled data,
ideally after every significant update to the pool of candidate papers. In
contrast, ILCiteR relies solely on distant supervision from a dynamic evidence
database and pre-trained Transformer-based Language Models without any model
training. We contribute a novel dataset for the evidence-grounded local
citation recommendation task and demonstrate the efficacy of our proposed
conditional neural rank-ensembling approach for re-ranking evidence spans.

现有的机器学习方法用于本地引用推荐直接将一个查询映射或翻译为值得引用的研究论文，这会导致有限的推荐可解释性。为了缓解这个问题，我们引入了基于证据的本地引用推荐任务，目标潜藏空间包括用于推荐特定论文的证据跨度。通过远程监督证据检索和多步骤再排序框架，我们的提议系统 ILCiteR 为查询推荐应引用的论文，并基于从现有研究文献中提取的类似证据跨度进行引用。与过去的方法只输出推荐不同，ILCiteR 检索了验证跨度和推荐论文的排序列表。其次，以前提出的引用推荐的神经模型需要在大规模标记数据上进行昂贵的训练，理想情况下是在候选论文池的每次重大更新之后。相反，ILCiteR 仅依靠动态证据数据库的远程监督和预训练的基于 Transformer 的语言模型，而无需任何模型训练。我们为基于证据的本地引用推荐任务贡献了一个新颖的数据集，并展示了我们提出的条件神经网络排序集成方法在重新排序证据跨度上的有效性。

ILCiteR: 拥有证据支持的可解释的本地引用推荐

ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation

The goal of local citation recommendation is to recommend a missing reference
from the local citation context and optionally also from the global context. To
balance the tradeoff between speed and accuracy of citation recommendation in
the context of a large-scale paper database, a viable approach is to first
prefetch a limited number of relevant documents using efficient ranking methods
and then to perform a fine-grained reranking using more sophisticated models.
In that vein, BM25 has been found to be a tough-to-beat approach to
prefetching, which is why recent work has focused mainly on the reranking step.
Even so, we explore prefetching with nearest neighbor search among text
embeddings constructed by a hierarchical attention network. When coupled with a
SciBERT reranker fine-tuned on local citation recommendation tasks, our
hierarchical Attention encoder (HAtten) achieves high prefetch recall for a
given number of candidates to be reranked. Consequently, our reranker requires
fewer prefetch candidates to rerank, yet still achieves state-of-the-art
performance on various local citation recommendation datasets such as ACL-200,
FullTextPeerRead, RefSeer, and arXiv.

通过使用最近邻搜索和层次化注意网络构建文本嵌入来预测相关的引用文献，并使用 SciBERT reranker 在本地引文推荐任务中进行微调，我们的 reranker 需要更少的预取候选项即可达到最先进的性能。

基于分层注意力文本编码器和 SciBERT 的本地引文推荐重新排序

Local Citation Recommendation with Hierarchical-Attention Text Encoder  and SciBERT-based Reranking

Citation recommendation systems aim to recommend citations for either a
complete paper or a small portion of text called a citation context. The
process of recommending citations for citation contexts is called local
citation recommendation and is the focus of this paper. Firstly, we develop
citation recommendation approaches based on embeddings, topic modeling, and
information retrieval techniques. We combine, for the first time to the best of
our knowledge, the best-performing algorithms into a semi-genetic hybrid
recommender system for citation recommendation. We evaluate the single
approaches and the hybrid approach offline based on several data sets, such as
the Microsoft Academic Graph (MAG) and the MAG in combination with arXiv and
ACL. We further conduct a user study for evaluating our approaches online. Our
evaluation results show that a hybrid model containing embedding and
information retrieval-based components outperforms its individual components
and further algorithms by a large margin.

本文旨在研究引用建议系统，在多个数据集上测试了基于嵌入、主题建模和信息检索技术的引用建议方法，并将它们结合成一个半遗传混合推荐系统进行了离线和在线的评估，结果表明包含嵌入和信息检索组件的混合模型优于其他单个方法和算法。