End-to-End Dynamic Query Memory Network for Entity-Value Independent Task-Oriented Dialog
Chien-Sheng Wu, Andrea Madotto, Genta Indra Winata, Pascale Fung
Abstract
In this paper, we propose an end-to-end Dynamic Query Memory Network (DQMemNN) with a delexicalization mechanism for task-oriented dialog systems. The added dynamic component enables memory networks to capture the dialog's sequential dependencies by using a context-based query. Besides, the delexicalization mechanism reduces learning complexity and it alleviates the out-of-vocabulary entity problems. Experiments show that DQMemNN outperforms original end-to-end memory network models on bAbI full-dialog task by 3.1 % per-response and 39.3% per-dialog accuracy. In addition, the proposed framework achieves a promising average per-response accuracy of 99.7% and per-dialog accuracy of 97.8% without hand-crafted rules and features.
BibTeX
@inproceedings{icassp2018_endtoenddynamicq,
title = {End-to-End Dynamic Query Memory Network for Entity-Value Independent Task-Oriented Dialog},
author = {Chien-Sheng Wu and Andrea Madotto and Genta Indra Winata and Pascale Fung},
booktitle = {ICASSP 2018},
year = {2018}
}