Do the Benefits of Joint Models for Relation Extraction Extend to
  Document-level Tasks?
Pratik Saini Tapas Nayak Indrajit Bhattacharya
Two distinct approaches have been proposed for relational triple extraction -
pipeline and joint. Joint models, which capture interactions across triples,
are the more recent development, and have been shown to outperform pipeline
models for sentence-level extraction tasks. Document-level extraction is a more
challenging setting where interactions across triples can be long-range, and
individual triples can also span across sentences. Joint models have not been
applied for document-level tasks so far. In this paper, we benchmark
state-of-the-art pipeline and joint extraction models on sentence-level as well
as document-level datasets. Our experiments show that while joint models
outperform pipeline models significantly for sentence-level extraction, their
performance drops sharply below that of pipeline models for the document-level
dataset.