Quantized-Dialog Language Model for Goal-Oriented Conversational Systems
Abstract
We propose a novel methodology to address dialog learning in the context of goal-oriented conversational systems. The key idea is to quantize the dialog space into clusters and create a language model across the clusters, thus allowing for an accurate choice of the next utterance in the conversation. The language model relies on n-grams associated with clusters of utterances. This quantized-dialog language model methodology has been applied to the end-to-end goal-oriented track of the latest Dialog System Technology Challenges (DSTC6). The objective is to find the correct system utterance from a pool of candidates in order to complete a dialog between a user and an automated restaurant-reservation system. Our results show that the technique proposed in this paper achieves high accuracy regarding selection of the correct candidate utterance, and outperforms other state-of-the-art approaches based on neural networks.
Cite
@article{arxiv.1812.10356,
title = {Quantized-Dialog Language Model for Goal-Oriented Conversational Systems},
author = {R. Chulaka Gunasekara and David Nahamoo and Lazaros C. Polymenakos and Jatin Ganhotra and Kshitij P. Fadnis},
journal= {arXiv preprint arXiv:1812.10356},
year = {2018}
}