柯佳伶Jia-Ling Koh廖盈翔Liao, Yin-Hsiang2020-12-142020-07-222020-12-142020http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G060647043S%22.&http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/111708noneAn automatic question generation (QG) system aims to produce questions from a text, such as a sentence or a paragraph. This system can be useful on the frontline of education, as making questions is a time-consuming and expert-participating craft. Traditional approaches are mainly based on heuristic and hand-crafted rules to transduce a declarative sentence into a related interrogative sentence. In this work, we propose a data-driven approach, which leverages a neural sequence-to-sequence framework with various transfer learning strategies to capture the underlying information of making a question, on a target domain with rare training pairs. Our experiment shows this modified model is capable to generate satisfactory results to some extent.nonequestion generationsequence-to-sequence modeltransfer learning