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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.acl-main.186.pdf