title: Finding a Jack-of-All-Trades: An Examination of Semi-supervised Learning in Reading Comprehension
abstract: Deep learning has proven useful on many NLP tasks including reading
  comprehension. However it requires a lot of training data which are not
  available in some domains of application. Hence we examine the possibility
  of using data-rich domains to pre-train models and then apply them in
  domains where training data are harder to get. Specifically, we train a
  neural-network-based model on two context-question-answer datasets, the
  BookTest and CNN/Daily Mail, and we monitor transfer to subsets of bAbI,
  a set of artificial tasks designed to test specific reasoning abilities, and of
  SQuAD, a question-answering dataset which is much closer to real-world
  applications. Our experiments show very limited transfer if the model isn’t
  shown any training examples from the target domain however the results
  are promising if the model is shown at least a few target-domain examples.
  Furthermore we show that the effect of pre-training is not limited to word
  embeddings.
authors: Rudolf Kadlec Ondřej Bajgar Peter Hrincar Jan Kleindienst
hIndex: Rudolf Kadlec 12