ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering
This work addresses the challenge of expensive data collection in question answering by enabling more effective transfer learning, though it appears incremental as it builds on existing MULT methods.
The paper tackles the problem of transfer learning for question answering by proposing ISS-MULT, an intelligent sample selection method that improves upon the MULT approach, showing performance gains particularly in answer triggering tasks across datasets like SQuAD and SelQA.
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.