  Distant supervision (DS) is a promising approach for relation extraction but
often suffers from the noisy label problem. Traditional DS methods usually
represent an entity pair as a bag of sentences and denoise labels using
multi-instance learning techniques. The bag-based paradigm, however, fails to
leverage the inter-sentence-level and the entity-level evidence for relation
extraction, and their denoising algorithms are often specialized and
complicated. In this paper, we propose a new DS paradigm--document-based
distant supervision, which models relation extraction as a document-based
machine reading comprehension (MRC) task. By re-organizing all sentences about
an entity as a document and extracting relations via querying the document with
relation-specific questions, the document-based DS paradigm can simultaneously
encode and exploit all sentence-level, inter-sentence-level, and entity-level
evidence. Furthermore, we design a new loss function--DSLoss (distant
supervision loss), which can effectively train MRC models using only
$\langle$document, question, answer$\rangle$ tuples, therefore noisy label
problem can be inherently resolved. Experiments show that our method achieves
new state-of-the-art DS performance.
