@inproceedings{yuan2019interactive,
author = {Yuan, Xingdi and Côté, Marc-Alexandre and Fu, Jie and Lin, Zhouhan and Pal, Christopher and Bengio, Yoshua and Trischler, Adam},
title = {Interactive Language Learning by Question Answering},
organization = {ACL},
booktitle = {EMNLP},
year = {2019},
month = {November},
abstract = {Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking component of comprehension. Such tasks present models with static documents that contain all necessary information, usually concentrated in a single short substring. Thus, models can achieve strong performance through simple word- and phrase-based pattern matching. We address this problem by formulating a novel text-based question answering task: Question Answering with Interactive Text (QAit). In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions. QAit poses questions about the existence, location, and attributes of objects found in the environment. The data is built using a text-based game generator that defines the underlying dynamics of interaction with the environment. We propose and evaluate a set of baseline models for the QAit task that includes deep reinforcement learning agents. Experiments show that the task presents a major challenge for machine reading systems, while humans solve it with relative ease.},
url = {http://approjects.co.za/?big=en-us/research/publication/interactive-language-learning-by-question-answering/},
}