  Transferring knowledge from a source domain to another domain is useful,
especially when gathering new data is very expensive and time-consuming. Deep
networks have been well-studied for question answering tasks in recent years;
however, no prominent research for transfer learning through deep neural
networks exists in the question answering field. In this paper, two main
methods (INIT and MULT) in this field are examined. Then, a new method named
Intelligent sample selection (ISS-MULT) is proposed to improve the MULT method
for question answering tasks. Different datasets, specificay SQuAD, SelQA,
WikiQA, NewWikiQA and InforBoxQA, are used for evaluation. Moreover, two
different tasks of question answering - answer selection and answer triggering
- are evaluated to examine the effectiveness of transfer learning. The results
show that using transfer learning generally improves the performance if the
corpora are related and are based on the same policy. In addition, using
ISS-MULT could finely improve the MULT method for question answering tasks, and
these improvements prove more significant in the answer triggering task.
