Learning Neural Textual Representationsfor Citation Recommendation
TABLE I: Hyperparameters used for training the proposed approach
['[BOLD] Hyperparameter', '[BOLD] Siamese network', '[BOLD] Triplet network']
The BM25 baseline is our implementation of the popular ranking function Okapi BM25 that is used effectively and efficiently in many information retrieval systems, including Elasticsearch. SubRef uses BM25 as the similarity function for document pairs and applies a submodular search with appropriate submodular scoring functions to choose the set of recommended citations. Citeomatic is a neural approach that first embeds all documents in a learned vector space, then selects the query’s nearest neighbours as candidates, and eventually reranks the candidates using a deep discriminative model to produce the final recommendation list. The learning rate used was SBERT’s default, while the batch size was set to 16 due to memory limitations. The value for θ was selected in [0.1−0.9] in 0.1 steps using the validation set. The number of epochs for all our models was set to have approximately equivalent training time. For all the compared models’ parameters we have used their default values.