Recursive Template-based Frame Generation for Task Oriented Dialog

Rashmi Gangadharaiah, Balakrishnan Narayanaswamy


Abstract
The Natural Language Understanding (NLU) component in task oriented dialog systems processes a user’s request and converts it into structured information that can be consumed by downstream components such as the Dialog State Tracker (DST). This information is typically represented as a semantic frame that captures the intent and slot-labels provided by the user. We first show that such a shallow representation is insufficient for complex dialog scenarios, because it does not capture the recursive nature inherent in many domains. We propose a recursive, hierarchical frame-based representation and show how to learn it from data. We formulate the frame generation task as a template-based tree decoding task, where the decoder recursively generates a template and then fills slot values into the template. We extend local tree-based loss functions with terms that provide global supervision and show how to optimize them end-to-end. We achieve a small improvement on the widely used ATIS dataset and a much larger improvement on a more complex dataset we describe here.
Anthology ID:
2020.acl-main.186
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2059–2064
Language:
URL:
https://aclanthology.org/2020.acl-main.186
DOI:
10.18653/v1/2020.acl-main.186
Bibkey:
Cite (ACL):
Rashmi Gangadharaiah and Balakrishnan Narayanaswamy. 2020. Recursive Template-based Frame Generation for Task Oriented Dialog. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2059–2064, Online. Association for Computational Linguistics.
Cite (Informal):
Recursive Template-based Frame Generation for Task Oriented Dialog (Gangadharaiah & Narayanaswamy, ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.186.pdf
Dataset:
 2020.acl-main.186.Dataset.zip
Video:
 http://slideslive.com/38928834