Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator
Abstract
User-machine interaction is crucial for information retrieval, especially for spoken content retrieval, because spoken content is difficult to browse, and speech recognition has a high degree of uncertainty. In interactive retrieval, the machine takes different actions to interact with the user to obtain better retrieval results; here it is critical to select the most efficient action. In previous work, deep Q-learning techniques were proposed to train an interactive retrieval system but rely on a hand-crafted user simulator; building a reliable user simulator is difficult. In this paper, we further improve the interactive spoken content retrieval framework by proposing a learnable user simulator which is jointly trained with interactive retrieval system, making the hand-crafted user simulator unnecessary. The experimental results show that the learned simulated users not only achieve larger rewards than the hand-crafted ones but act more like real users.
Cite
@article{arxiv.1804.00318,
title = {Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator},
author = {Pei-Hung Chung and Kuan Tung and Ching-Lun Tai and Hung-Yi Lee},
journal= {arXiv preprint arXiv:1804.00318},
year = {2018}
}