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Task-Driven and Experience-Based Question Answering Corpus for In-Home Robot Application in the House3D Virtual Environment
Zhuoqun Xu, Liubo Ouyang, Yang Liu
At present, more and more work has begun to pay attention to the long-term housekeeping robot scene. Naturally, we wonder whether the robot can answer the questions raised by the owner according to the actual situation at home. These questions usually do not have a clear text context, are directly related to the actual scene, and it is difficult to find the answer from the general knowledge base (such as Wikipedia). Therefore, the experience accumulated from the task seems to be a more natural choice. We present a corpus called TEQA (task-driven and experience-based question answering) in the long-term household task. Based on a popular in-house virtual environment (AI2-THOR) and agent task experiences of ALFRED, we design six types of questions along with answering including 24 question templates, 37 answer templates, and nearly 10k different question answering pairs. Our corpus aims at investigating the ability of task experience understanding of agents for the daily question answering scenario on the ALFRED dataset.
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