  Information Extraction (IE) refers to automatically extracting structured
relation tuples from unstructured texts. Common IE solutions, including
Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence
tuples, and are severely restricted by limited relation types as well as
informal relation specifications (e.g., free-text based relation tuples). In
order to overcome these weaknesses, we propose a novel IE framework named
QA4IE, which leverages the flexible question answering (QA) approaches to
produce high quality relation triples across sentences. Based on the framework,
we develop a large IE benchmark with high quality human evaluation. This
benchmark contains 293K documents, 2M golden relation triples, and 636 relation
types. We compare our system with some IE baselines on our benchmark and the
results show that our system achieves great improvements.
