出版社：The Japanese Society for Artificial Intelligence
摘要：The performance of reading comprehension, which is a question answering technique, by deep neural networks is now comparable to that of humans. However, there are still problems with the reading comprehension when given ambiguous questions. In this work, we propose a novel task called Specific Question Generation (SQG). SQG specifically revises the input question and suggests several specific question (SQ) candidates so that users can choose the SQ that is closest to their intent and obtain a highly accurate answer from the reading comprehension. We also propose a Specific Question Generation Model (SQGM) for facilitating the SQG. This model is based on an encoder-decoder model and uses two copy mechanisms (question copy and passage copy). The key idea here is that the missing information in the user-input question is described in the passage. Experimental results with public reading comprehension datasets demonstrated that our model generated specific questions that can improve reading comprehension accuracy.
