  In this work, we introduce the task of Open-Type Relation Argument Extraction
(ORAE): Given a corpus, a query entity Q and a knowledge base relation (e.g.,"Q
authored notable work with title X"), the model has to extract an argument of
non-standard entity type (entities that cannot be extracted by a standard named
entity tagger, e.g. X: the title of a book or a work of art) from the corpus. A
distantly supervised dataset based on WikiData relations is obtained and
released to address the task.
  We develop and compare a wide range of neural models for this task yielding
large improvements over a strong baseline obtained with a neural question
answering system. The impact of different sentence encoding architectures and
answer extraction methods is systematically compared. An encoder based on gated
recurrent units combined with a conditional random fields tagger gives the best
results.
