SPECTER: Document-level Representation Learning using Citation-informed Transformers
This addresses the need for better document-level representations in NLP for scientific applications like classification and recommendation, though it is incremental as it builds on existing Transformer models.
The authors tackled the problem of generating document-level embeddings for scientific documents by pretraining a Transformer model using citation graph information, resulting in SPECTER outperforming competitive baselines on a new benchmark called SciDocs.
Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, the embeddings power strong performance on end tasks. We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that SPECTER outperforms a variety of competitive baselines on the benchmark.