Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models
This addresses the challenge for scientists in determining where to cite sources, potentially improving the robustness of scientific arguments, though it is incremental in applying existing deep learning techniques to a specific domain.
The paper tackles the problem of automatically detecting sentences that need citations in scientific writing, proposing an attention-based BiLSTM model that achieves state-of-the-art performance with an F1 score of 0.507 on a standard dataset and 0.856 on a new large dataset.
Scientist learn early on how to cite scientific sources to support their claims. Sometimes, however, scientists have challenges determining where a citation should be situated -- or, even worse, fail to cite a source altogether. Automatically detecting sentences that need a citation (i.e., citation worthiness) could solve both of these issues, leading to more robust and well-constructed scientific arguments. Previous researchers have applied machine learning to this task but have used small datasets and models that do not take advantage of recent algorithmic developments such as attention mechanisms in deep learning. We hypothesize that we can develop significantly accurate deep learning architectures that learn from large supervised datasets constructed from open access publications. In this work, we propose a Bidirectional Long Short-Term Memory (BiLSTM) network with attention mechanism and contextual information to detect sentences that need citations. We also produce a new, large dataset (PMOA-CITE) based on PubMed Open Access Subset, which is orders of magnitude larger than previous datasets. Our experiments show that our architecture achieves state of the art performance on the standard ACL-ARC dataset ($F_{1}=0.507$) and exhibits high performance ($F_{1}=0.856$) on the new PMOA-CITE. Moreover, we show that it can transfer learning across these datasets. We further use interpretable models to illuminate how specific language is used to promote and inhibit citations. We discover that sections and surrounding sentences are crucial for our improved predictions. We further examined purported mispredictions of the model, and uncovered systematic human mistakes in citation behavior and source data. This opens the door for our model to check documents during pre-submission and pre-archival procedures. We make this new dataset, the code, and a web-based tool available to the community.