Multi-level Memory for Task Oriented Dialogs

TL;DR

This paper introduces a multi-level memory architecture for task-oriented dialog systems that separates dialog context and knowledge base results, improving reasoning and performance over existing models.

Contribution

It proposes a novel multi-level memory structure that relaxes previous assumptions, enabling more effective reasoning in dialog systems.

Findings

Outperforms state-of-the-art models on three datasets

Achieves 15-25% higher entity F1 and BLEU scores

Demonstrates better reasoning over knowledge base results

Abstract

Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base, such as the use of triples to represent knowledge, and combines dialog utterances (context) as well as knowledge base (KB) results as part of the same memory. This causes an explosion in the memory size, and makes the reasoning over memory harder. In addition, such a memory design forces hierarchical properties of the data to be fit into a triple structure of memory. This requires the memory reader to infer relationships across otherwise connected attributes. In this paper we relax the strong assumptions made by existing architectures and separate memories used for modeling dialog context and KB results. Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results. The multi-level memory first addresses queries, followed by results and finally each key-value pair within a result. We conduct detailed experiments on three publicly available task oriented dialog data sets and we find that our method conclusively outperforms current state-of-the-art models. We report a 15-25% increase in both entity F1 and BLEU scores.