Multi-Level Memory for Task Oriented Dialogs

Revanth Gangi Reddy, Danish Contractor, Dinesh Raghu, Sachindra Joshi


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.
Anthology ID:
N19-1375
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3744–3754
Language:
URL:
https://aclanthology.org/N19-1375
DOI:
10.18653/v1/N19-1375
Bibkey:
Cite (ACL):
Revanth Gangi Reddy, Danish Contractor, Dinesh Raghu, and Sachindra Joshi. 2019. Multi-Level Memory for Task Oriented Dialogs. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3744–3754, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Multi-Level Memory for Task Oriented Dialogs (Gangi Reddy et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1375.pdf
Code
 DineshRaghu/multi-level-memory-network
Data
Frames Dataset