Answering Mixed Type Questions about Daily Living Episodes

We propose a physical-world question-answering (QA) method, where the system answers a text question about the physical world by searching a given sequence of sentences about daily-life episodes. To address various information needs in a physical world situation, the physical-world QA methods have to generate mixed-type responses (e.g. word sequence, word set, number, and time as well as a single word) according to the content of questions, after reading physical-world event stories. Most existing methods only provide words or choose answers from multiple candidates. In this paper, we use multiple decoders to generate a mixed-type answer encoding daily episodes with a memory architecture that can capture short- and long-term event dependencies. Results using house-activity stories show that the use of multiple decoders with memory components is effective for answering various physical-world QA questions.